{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Function Approximation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Previous we tried using manually created buckets to discretize continuous states, effectively mapping continuous observations to discrete states. Great, but, isn't that what Supervised Learning is for? How about we use the power of function approximators to do that mapping for us???\n",
    "\n",
    "Let's do just that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tempfile\n",
    "import base64\n",
    "import pprint\n",
    "import random\n",
    "import json\n",
    "import sys\n",
    "import gym\n",
    "import io\n",
    "\n",
    "from gym import wrappers\n",
    "from collections import deque\n",
    "from subprocess import check_output\n",
    "from IPython.display import HTML\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As with previous notebooks we will use an action selection function to give us an exploration strategy. Feel free to change this function and play with it to see what happens. For now, let me just provide a couple function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def action_selection(state, model, episode, n_episodes):\n",
    "    epsilon = 0.99 if episode < n_episodes//4 else 0.33 if episode < n_episodes//2 else 0.\n",
    "    values = model.predict(state.reshape(1, state.shape[0]))[0]\n",
    "    if np.random.random() < epsilon:\n",
    "        action = np.random.randint(len(values))\n",
    "    else:\n",
    "        action = np.argmax(values)\n",
    "    return action, epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def neuro_q_learning(env, gamma = 0.99):\n",
    "    nS = env.observation_space.shape[0]\n",
    "    nA = env.env.action_space.n\n",
    "    \n",
    "    # memory bank\n",
    "    memory_bank = deque()\n",
    "    memory_bank_size = 100000\n",
    "    \n",
    "    # function approximator\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_dim=nS, activation='relu'))\n",
    "    model.add(Dense(nA, activation='linear'))\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "\n",
    "    # constant values\n",
    "    n_episodes = 50000\n",
    "    batch_size = 256\n",
    "    training_frequency = 20\n",
    "    \n",
    "    # for statistics\n",
    "    epsilons = []\n",
    "    states = []\n",
    "    actions = []\n",
    "    \n",
    "    # interactions\n",
    "    for episode in range(n_episodes):\n",
    "        state = env.reset()\n",
    "        done = False\n",
    "        \n",
    "        # each episode\n",
    "        while not done:\n",
    "            states.append(state)\n",
    "            \n",
    "            # select action\n",
    "            action, epsilon = action_selection(state, model, episode, n_episodes)\n",
    "            epsilons.append(epsilon)\n",
    "            actions.append(action)\n",
    "            \n",
    "            # save history in memory bank\n",
    "            nstate, reward, done, info = env.step(action)\n",
    "            memory_bank.append((state, action, reward, nstate, done))\n",
    "            if len(memory_bank) > memory_bank_size:\n",
    "                memory_bank.popleft()\n",
    "            \n",
    "            # iterate to next state\n",
    "            state = nstate\n",
    "\n",
    "        # only every few episodes enter training and update neural network weights\n",
    "        if episode % training_frequency == 0 and len(memory_bank) == memory_bank_size:\n",
    "            \n",
    "            # randomly select batches of samples from the history\n",
    "            # for training to prevent values spiking due to high \n",
    "            # correlation of sequential values\n",
    "            minibatch = np.array(random.sample(memory_bank, batch_size))\n",
    "\n",
    "            # extract values by type from the minibatch\n",
    "            state_batch = np.array(minibatch[:,0].tolist())\n",
    "            action_batch = np.array(minibatch[:,1].tolist())\n",
    "            rewards_batch = np.array(minibatch[:,2].tolist())\n",
    "            state_prime_batch = np.array(minibatch[:,3].tolist())\n",
    "            is_terminal_batch = np.array(minibatch[:,4].tolist())\n",
    "\n",
    "            # use the current neural network to predict \n",
    "            # current state values and next state values\n",
    "            state_value_batch = model.predict(state_batch)\n",
    "            next_state_value_batch = model.predict(state_prime_batch)\n",
    "\n",
    "            # update the state values given the batch\n",
    "            for i in range(len(minibatch)):\n",
    "                if is_terminal_batch[i]:\n",
    "                    state_value_batch[i, action_batch[i]] = rewards_batch[i]\n",
    "                else:\n",
    "                    state_value_batch[i, action_batch[i]] = rewards_batch[i] + gamma * np.max(next_state_value_batch[i])\n",
    "            \n",
    "            # update the neural network weights\n",
    "            model.train_on_batch(state_batch, state_value_batch)\n",
    "\n",
    "    return model, (epsilons, states, actions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Did you see that `neuro_q_learning` function??? I had to add comments to this one to make it easier to understand. There are a couple of things that you might want to read about. Check out the \"Further reading\" sections on the README.md files for that. \n",
    "\n",
    "For example, did you see we are now saving experiences into a `memory_bank`? That is called experience replay. Now, we don't just go over the saved tuples sequentially, we randomly select a batch of sample and update the neural network weights with that. This is important often due to the high correlation of the samples.\n",
    "\n",
    "Also, did you see how we update the `state_value_batch` with each sample? That is just very cool, we are not using `alpha` here anymore, the learning rate is now used for gradient descent when we are learning the network weights.\n",
    "\n",
    "Ok, let's run this algorithm!\n",
    "\n",
    "Beware, this will take some time... 15-25 min, so go get your favorite beverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:34:34,843] Making new env: CartPole-v0\n",
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     ]
    }
   ],
   "source": [
    "mdir = tempfile.mkdtemp()\n",
    "env = gym.make('CartPole-v0')\n",
    "env = wrappers.Monitor(env, mdir, force=True)\n",
    "\n",
    "model, stats = neuro_q_learning(env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Hope you enjoyed your vacations. :)\n",
    "\n",
    "Back to it. Let's look at a couple of episodes to see how the agent did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h2>Episode 0<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source src=\"data:video/mp4;base64,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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 10000<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source src=\"data:video/mp4;base64,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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 29000<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source src=\"data:video/mp4;base64,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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 49000<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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type=\"video/mp4\" />\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos = np.array(env.videos)\n",
    "n_videos = 4\n",
    "\n",
    "idxs = np.linspace(0, len(videos) - 1, n_videos).astype(int)\n",
    "videos = videos[idxs,:]\n",
    "\n",
    "strm = ''\n",
    "for video_path, meta_path in videos:\n",
    "    video = io.open(video_path, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    \n",
    "    with open(meta_path) as data_file:    \n",
    "        meta = json.load(data_file)\n",
    "\n",
    "    html_tag = \"\"\"\n",
    "    <h2>{0}<h2/>\n",
    "    <video width=\"960\" height=\"540\" controls>\n",
    "        <source src=\"data:video/mp4;base64,{1}\" type=\"video/mp4\" />\n",
    "    </video>\"\"\"\n",
    "    strm += html_tag.format('Episode ' + str(meta['episode_id']), encoded.decode('ascii'))\n",
    "HTML(data=strm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's close up the environment and upload (in case you want to)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:51:32,930] Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/tmpkc1h_x_k')\n"
     ]
    }
   ],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:51:32,974] [CartPole-v0] Uploading 50000 episodes of training data\n",
      "[2017-04-26 19:51:37,095] [CartPole-v0] Uploading videos of 59 training episodes (294651 bytes)\n",
      "[2017-04-26 19:51:37,736] [CartPole-v0] Creating evaluation object from /tmp/tmpkc1h_x_k with learning curve and training video\n",
      "[2017-04-26 19:51:37,978] \n",
      "****************************************************\n",
      "You successfully uploaded your evaluation on CartPole-v0 to\n",
      "OpenAI Gym! You can find it at:\n",
      "\n",
      "    https://gym.openai.com/evaluations/eval_ansfccvzQBGTWxZV1g3SzA\n",
      "\n",
      "****************************************************\n"
     ]
    }
   ],
   "source": [
    "gym.upload(mdir, api_key='<YOUR API KEY>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, let's look at some of the statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "epsilons, states, actions = stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fad065d8a90>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x17yaU37MZguFZXoXzU7aDhyf9KBVdbz7/TDwD/R20K8mOQug+/3wGjWu1r59SDurjDGq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\nPwWfT9n0Hzj+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad582d59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(epsilons)), epsilons, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tnWMfkqRFdqwuc60DtrX1bcAVQ/Xba+AB4OQkpwOXAruq6kBVHQR2AWvbtjdW1deqqoDb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\nuhkmkqRu/x+aGrpoBynB9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad05fbdeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bins = np.histogram(actions, bins=3)\n",
    "width = 0.7 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Cool, not bad at all!\n",
    "\n",
    "Let's test this fully trained agent and see how it does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:51:42,647] Making new env: CartPole-v0\n",
      "[2017-04-26 19:51:42,666] Starting new video recorder writing to /tmp/tmpbj36uk9x/openaigym.video.1.1418.video000000.mp4\n",
      "[2017-04-26 19:51:44,091] Starting new video recorder writing to /tmp/tmpbj36uk9x/openaigym.video.1.1418.video000001.mp4\n",
      "[2017-04-26 19:51:45,938] Starting new video recorder writing to /tmp/tmpbj36uk9x/openaigym.video.1.1418.video000008.mp4\n",
      "[2017-04-26 19:51:47,948] Starting new video recorder writing to /tmp/tmpbj36uk9x/openaigym.video.1.1418.video000027.mp4\n",
      "[2017-04-26 19:51:51,204] Starting new video recorder writing to /tmp/tmpbj36uk9x/openaigym.video.1.1418.video000064.mp4\n"
     ]
    }
   ],
   "source": [
    "mdir = tempfile.mkdtemp()\n",
    "env = gym.make('CartPole-v0')\n",
    "env = wrappers.Monitor(env, mdir, force=True)\n",
    "\n",
    "for episode in range(100):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = np.argmax(model.predict(state.reshape(1, 4))[0])\n",
    "        nstate, reward, done, info = env.step(action)\n",
    "        state = nstate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h2>Episode 0<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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p/7qZuhEhzegwHI1I9AtB7hI6vuLEqp2bEjr2yRBxtVDqrH+zpM8TcPGOOUtWaC2/U9y80WyJ3o8XvcAAABhQZokSahBbJlMCGf//p4QAABHeAOQoAG36tUkWpIZrccz5Mo9PgHeJmE6EnMLy3jhfx4LhU7rPUbW1ok1fvGQiCiSYRU7JNX3XUUDQ777SzM4UxeufO71XJlgjHPzasAAMAAAADtBnkJFFSwj/wAAF0KsRck3NV71SNBE1jQY8itTMhbnauZP/3o02asdf5b3zAgANvDoqNtI9o9p4DB8wQAAAEABnmF0R/8AACSr+Fh2ZCoIozyGdmjnDLXG38HY1Mga9K4JYzs9O6exFgBRdABZ24ByeDV0m0i69evI1HzZHVNgAAAAQwGeY2pH/wAAJK5e0LPxmbYnP9sun720JgAsf7etncd4xEtol90zcTKWmZ5BGV7+73MMNU6h/JNApCrhVxcsoepO/pkAAABZQZpoSahBbJlMCGf//p4QAABHljmXobeUzbFqbwuqNSYvCWr18blyjD7CV1U1F5smk5kpoqgRzTABDv4XnnIt+OnerwYc2vaeI/GkEV+1gb92pD8n6nk6YH0AAAA+QZ6GRRUsI/8AABdQMLhiQSnqgj/UPbhVgyR73oeEnZeLQAjtfhp9nlp4a1SSgLqpQZq1x/IXdsXKfIncs/MAAAA+AZ6ldEf/AAAkqepnXCYFSVJPY6MgKK+Nw16X7PlNDMfSsNQawyEiDKlyfUnIKkxy4AH3VP5ZLxvnVZq1LJsAAAAvAZ6nakf/AAAkrl5pLe9UNa2tyg7vmLEbcMd+jycZWVTQD+AJdD/T093QbJBQSbAAAABpQZqsSahBbJlMCGf//p4QAABHnpGOxFWFOHQ/+5h/E8wUBezksPAl/9NIzNqIhPK6pOShMCBFVpra/+ADlD+z6oRfZITDm0mtAmLrDFGOm7UN5Rc49igwbYtPAlkCwKYZzNEQFLADeA2AAAAARUGeykUVLCP/AAAXQqyN+SCRjT0lARZ22C5Qi5ojQse621XcyqTqJKPj6qDhNEwm8ZHpT/gYr7qic2CDXKjVrZjFZKMjAwAAAD8Bnul0R/8AACSfEwb3G2JWzp93HlMEvaAnjVM4/6ULSQo4LjoWejhgAQdT678NHWpRoHIJ00NwekoSk2nP6kgAAAAuAZ7rakf/AAAkrl/ub/a8f+GJO16TRfPUbsX10tg+/hhiiGHYpwIx04bcYZeo/wAAAJ5BmvBJqEFsmUwIZ//+nhAAAElDj0Va40quO68h4FEARKTmR3U9gG0Ky/ybdhfyLCx2YBXrNFh7nxWHD2knG+WeCvDLHGzD22rmPq3tN/wuMGx/2RCCse8Fy7VxC+4pP7GVF2+efITQJTf7S8s08z9rMCPf2fSjI68Q1NQA/EAzsHROhs1tvsGk6L/+uJ1lX2mY5XTXB6gx9TffWBJqgwAAAEBBnw5FFSwj/wAAF+l8w2AqcW48M4wAEg8kD1l9sGC2xSCwVhgn7TXWMARGB98vhefXnoNRdqWxRJlrrkl+NRNbAAAAOgGfLXRH/wAAJJKo3E9GRqD78zlxcK1i525+GVFgQ0w2MfUIAA/CHh5mu7pz6m6eVBYDZ1X0jOA/iLEAAAA/AZ8vakf/AAAlvvU+GmVie5KeIAALY7Nj7aJPr7Nb8ksvLjSWyd8brhduqHBLzmgjCR/PinsffNgBqo0O7sqAAAAAaUGbNEmoQWyZTAhn//6eEAAASVSc++ZQhlhFAAjFZlfGCVSOtNABZEy5y2yyglGLBkTqjx/1+w3uTL5f7ns3tgn+jDbK+t3KlrDCFecr1bftxxY7EvURycspHbKDJL3Lqb/UsOl14NjJgAAAAFBBn1JFFSwj/wAAF+ANToAdHnv7wNnpw9n1iqSsc9bgWYBMLcD7iaZZhhHDxX7giFHO+8iUjgKUAbl3nBpWf76iFX+306QV5Xk17DdHgn7TAwAAAEUBn3F0R/8AACXAmJkazNEHDsJ4y3k0D3HL0DXes3N71kIBvrSwJkbABK+hX4PuhfGcOGzTBUAvuEjPKajO9tD8+FXyk4AAAABJAZ9zakf/AAAlvYM3gL/jl9lcuHModdLr+QFSeZbMLee8tvu09DAAA9rQW9htm6U7fSRSRid/ZnbNCCOTv23KSuBncSyV0u8QIAAAAHVBm3hJqEFsmUwIZ//+nhAAAElDzq+ADnzFo+GDaPEn0HvP3YV8gojTF0Xz6k9ODmPffpgt40WX8KjbtQoAm4Sn44b9YZCSxplYG1Tvc1HNXVSE5kMzpVrHSW9tZGPxqizy/G5MsdNZ6W9cSeaKH/fo4o1OVxkAAAAxQZ+WRRUsI/8AABff6+i0PNPMS+7B89YgYUxE02bpK+3WCymz4GrijVeMpNylUYxAgAAAADsBn7V0R/8AACWp6ccThXRnMcwiK41Nk9D2ujC2QEtKfc+4AG1EtPhELYVdxsuzYHadRbAECs+NRqUC2QAAACsBn7dqR/8AACWuXjOBeUOzzbe5Bdk06mW2li2V5n52F8pHxMmJihCmsSTBAAAAa0GbvEmoQWyZTAhn//6eEAAASUPoWwm3ldHF+KgAXXRoEoKx6ilVBThr+n7Iq2rbA9r6Kerh/u6WAWk/NFO+dIqOwChQA6EZhDRSMuzhsR1FSU5Y9Q47bmcOv4xzSnFqgN6Dakb67KSD4zcEAAAAP0Gf2kUVLCP/AAAX6FsJAAF0d8hG/3ghMKTNt898k1vkYUlz2tKkZzUoiQAs3ZaAJOaQytQhA4OYCLzveSDCBQAAAEIBn/l0R/8AACU7fufN4pYA2GZdviOB+a9JozdE/buulaFWlwrdLYMod40/k8rONRlS1TuoF2eg893sy4Doi24WGfAAAAA/AZ/7akf/AAAlrl/7YosteQnbF9qEumN0m1ZIweoYAWeGZN8d+iUlUACU46uthMRlc8yRhgSuQF+DjTH5cJAhAAAAb0Gb4EmoQWyZTAhn//6eEAAAS1SiFj8ANtEURwCIj5YL8eqIwfOyMnEckq1fHO013ev2E5J67gZGq59ofvoa4vv/9hYQRja8BP3XRAs5tL/LrIGOODNoG3ZKmWOtIwiPCGX6qNQ2lwdqwNEA3Xo0cQAAAD5Bnh5FFSwj/wAAGImC7bAEQzwwAtcx1R5WipxCqSr2mwqu3l35vPhNP0nV2yfU9C9p05di2svpAxvXska7gAAAADwBnj10R/8AACXAmpXIiDUUZceZbhPY68TlQlXkGXWyimbAAnGiAPBCRqfmmIy0q5tYDeXghU44c23gxAgAAAA4AZ4/akf/AAAmvwJkjLbcOxdG187WLYzKhdl2KrPV7uNHHdNnPwAffEKOmDvQ97yRV5EZDowmOmEAAAB2QZokSahBbJlMCGf//p4QAABLUPSVjwtAC/GX6UIxN/46DgLy7Sc7DowjDuDT3vH8HAxY4RtkTW5sIVBgjTWSjjcEM2/uBfZLUQ2x6hxs9h6+bYJv8pxApfnSlsmq//DhBHiilqlB4Q4EyLlU6IK0+DOso2k7rgAAAEtBnkJFFSwj/wAAGH9DvQtbYUiX+0aNiVkjcwYk4KlrNJz+EFnmaJXoxaAEI6tJTiYF8AJkeg4W5Lnu2BTwmI388e/IvsC5aX4vAeEAAABHAZ5hdEf/AAAmqelcKyNyho8tLkQ47ZgKVihLYicKjmZkbI5ax2jR5+AiZrIGgBILT+4TC8TE1yNoZQWsj55NQkk9hu9Mo1YAAABGAZ5jakf/AAAmrYzywK/y4LAzl8sAg6a1mKgJjpcbyLlvwPskhkMCDmVB0ftFPYAP3HhA1Ik8YcsLLmg2zcsWtr91Y5TKgQAAAExBmmhJqEFsmUwIZ//+nhAAAEtD6FsILOgwBaCKgx5NguTLc1/xjIzzkJcL0Vc5akFEAnj46UccmvGDB6VygPHwZH1Kk1jisuMaesoRAAAAUEGehkUVLCP/AAAYiJtN9RNj2ajJSsMkbASH4rT0eO5FekEY8yD1tsnQu3kr3N/B47ANgytC7dI3sEHd9ZM7wK3DbjyyKNttITGcrUnji7KLAAAASwGepXRH/wAAJsCZ3ekw0fCC8CiyKPymI+5wmE+0sKjbXMOtzpO4GJO3Rq8dDbyrnFwMACWq0d9YP4LN+8sOkpk9evc55Rur4HXmCQAAAEsBnqdqR/8AACatW+Z64AZ23pu/e15AbWuEFURAC21Ek0DU0aJlmz87NWXnQLJ2EZ41/9Xwz5kXJIEwDcGQQwqxI49ETCo23171mCAAAABZQZqsSahBbJlMCGf//p4QAABNUPSY540ylc2cujf4lOR58hBOCibxqGE6e0lz+qh4iU0d0rh9IY16yQze2oWAQI8NkausgAGq/gqjnf7vnR9as+ywhyxJrYQAAABHQZ7KRRUsI/8AABkpfE72E9j/PxQEZFYn/srItfQ/9m9HvieplheFksde59CKW5DXwACW8kzpkk+35bGm2dF9riuFmAZXVMEAAABSAZ7pdEf/AAAmqescc7awWJFJ5xHNgAuGEJjvuh7Xd7j/5rSaPRfMCmB5xKaT/uL7QdSu5MU8C+De8Yy7ibiLD62zT0aKpjWxM3/TMKaOUCarJwAAAEQBnutqR/8AACfZbji2NivFKMC+w0/Pw9yKPMZ4g2Rs8AF1EFv7yPV0ov5vKwepxtoog0igRHGGaZ95Lz071KzFIllnhAAAAGxBmvBJqEFsmUwIZ//+nhAAAE1D99w4szQD1FF4OmnPGVETL4kIs1/z+Cr1+9+BTfnqiWCcHRi47TrxE/7Hokq5YgzmJrYALqPuFp1QnDCq2lYGOcdvZ+Tbz26Cx5hwsHOtqJWTaQHOQXOc2CEAAABBQZ8ORRUsI/8AABkfQ70LXmngxfghAnTAXsgFCYSTDnTOqvgj08GkGB9/BQlGXFdIR5pHGNnXomA6BNYuOz4KEEEAAABPAZ8tdEf/AAAn21ViTVzYOg5HdebsAHBep/afk0DdZYHGaC0ThhQ1gGY703J+l56YDyPSa9hqz5etoVseWgmx7UYLm85n9bh+dcDH7JqCwQAAAEkBny9qR/8AACfEGZfHueZX2qMttrPrDPjph7ExMzIsO0NoG1lO18YpwAcFm3ss3ArHnM7hMcJU9sdiIRz8Uv/3OcHlnYd5NAe4AAAAZEGbNEmoQWyZTAhn//6eEAAATUP36wm3ldH0kyRoQCMGKlj3cJ7oMi4lUpADyAx5sg7vHRhAzMboNsRChNECNmiwyx9oYp58DH9sGR71hU5z9ul+59cgTnf/YfP5X+D1twp0v4sAAABEQZ9SRRUsI/8AABkfQ96ppG5Kdjmw2XKUNvOIUQJDsxANZuaaBm3SHkrdT0ybSOOIll6yYnZLz80xKSa7GxK3NrSXegsAAAA5AZ9xdEf/AAAn21YwqY7/WVES3ffyplPAd4ASObiH8ATKy909ujKC4lmJdVmQozEHH57UxphMP28oAAAAYwGfc2pH/wAAJ9czg5u7iD4lFr4yOw9KAEtRR24AhcPUtm3C/YR5lUWK3/Y1Y5MPCDvgwr3YnWTiPyhR2RFHQHh+SKfMLPnoicU46gvroaTU7zIRSbnIrigdCLVNoetTScW8oAAAAHVBm3hJqEFsmUwIZ//+nhAAAE9rnxn1f77ABYKVP7LR+EKZcDqYW1I486Evtahe6Stk/XdvufLlbE8hxnObVn4wJ792lfTVBdILP6D1w5ToTfUZIqwOZcj/xSrzB85xyxjXZ+9kWGunXJOy/xcLw3FXHhsn2oEAAABFQZ+WRRUsI/8AABm/Q9Q4ENdQ4jZQAkurSlkpk0aQ8DpzhDiZnHsyVpNk2uvvFfyPFK08JALFeVUuTV+1TysrQqUOeA0IAAAAUgGftXRH/wAAKP5YkXzcNggERnlUcwDUiHbaxTFsQu22iMzwy9LQASoWENy6T92CMEgZiyBl9yZANWv4chKWLqUlMw4r8jygNtyWx5LOQIi2t3EAAABLAZ+3akf/AAAo5BkcFIEZ1q28Et6gAugAz4RB63FKwiV9VsJeA1OOjrmiPIFk57G+hfHoHcA8+mIPMVcQY4fTpGTJHM42KDXWhcqBAAAAcUGbvEmoQWyZTAhn//6eEAAAT2ufK3K3k6VQUEgACFQC55nLWehnnyIB8xyTHuDuvTslld/LmiBySKpy5HuigthSy5vbCz0eYAhDLJPueO43V1dSuf9mmRgCjRJIdqgtAYmwNqPx/5YZSItPN5l5oJloAAAAREGf2kUVLCP/AAAZyHALRfkgBYHMp5mRKhBWe9serltp6jtospVDDMH8ye5gjVC75y3gcJYglKLAz9dMn2YNgBdnIMCBAAAAOAGf+XRH/wAAKPqdjWIn2bVe6I9D8z2znQttf/KQTe3zmYUnGvWAAg72Bkezwon6BoKD//nzXZP8AAAAOgGf+2pH/wAAKOQZMnD6fUHby8oFGbsNvXQng7cRiB453/OXAA2GzP4RPdodCXnnRN/53dNtdaX6mVEAAABeQZvgSahBbJlMCGf//p4QAABRwWJWvmy9BDoXCb3Ej31NXbP36Ab+B7csHE2ZzdEuUgDCOtXncggvAqTNH90ABLBnAeMIv6sP+nDftWP3f8wltx4P1r2QRHVfv6WcoQAAAE9Bnh5FFSwj/wAAGml8cXYT3/OoBQAQX67V5n6pXWtu+OZd0pVjtfGxoVdd1JvVcIn49j33ss1Jp2JYGqXbMI6FTz87IJGdsoFdIEmNlMqAAAAALwGePXRH/wAAKOBZoTec0Iearf1zk6Ia3XDS2x5tf+b6xQncbEwjlPsoaFvsKD8wAAAANgGeP2pH/wAAKhls+5YR2+icSzGY/CH6CG11oCH25NfV1Dytmwvxty64uj/LzWHzBiwiph/+YQAAAGpBmiRJqEFsmUwIZ//+nhAAAFHUZ+ZYWnJezJMDTl/MsQMnndlHkfm1MCguSBbDdzB6O/Z0a0bRuF55ZHSxSR9MlpNFDoXhXS123QiA3DJD/MmhsnMQxOHWyu2xdNAQDTw2tEX5XQ8YMMpYAAAALkGeQkUVLCP/AAAaX3UdC15p5dZ8aGZc0g3eeT2o9JEHm4TsSsfy3npLARLsjhcAAABAAZ5hdEf/AAAqGp2NYigKy6eL4QwtozfXNMITjeZt9ZL2hmRiKY/kMrv5A+4AAnPwiPS/jgG/MLMnmZPzaoeKygAAAEIBnmNqR/8AACoEGTJw3zxPQC0WcOlPhG1nbxEiZUGyZGPk4XlpAmnq1wALhhD4b/2foSwCnMgyFDq3yxv7sZ76XcsAAABlQZpoSahBbJlMCGf//p4QAABT67JXdLV29h8qYEvUIMrsSMgKfASq7/Mf93kon552Wla4kQ+Sn6sr2D8jSMwnlrQv7XuCVq4snBuH+qfF72KB4MeLdd3qJv+wLK4C7oexCS1VMxkAAABGQZ6GRRUsI/8AABsJfHFJgtmOAc2GcHzqakYF2D2bkdNL72ai4ik44mF8f/n6JLntxZqa0AC6AkzazIy/oEg8cTTKM6PNwQAAADMBnqV0R/8AACoanShAe++ZGbxd8ZSgU3w6LZqK+Q0gIaVglVNRBDunxfxRfM6DMmInEU0AAABAAZ6nakf/AAArOWz7lifoajFmIzQKTzf2MCGpMLrSH37WkACduhT2zITqbipnTJjl7fIALNLvIFBWDTpNTeBZeAAAAGVBmqxJqEFsmUwIZ//+nhAAAFQUeusMZKAFsQqeNDsrM+CwvOEM6Tw/uvQmpY3EBjkU8FGY/tEI5/uHZWQxHXJLdhTV7J5U3uQJi8P9gwVS9Y/92fdsGvlDUlacQtqf0ZV7m3wJQAAAAEZBnspFFSwj/wAAGwh0bB/Hs+4MlMyXPcXRozKnYDixMVQhgR2Vi6WAAP53jcS+FfmiVqoLppXjA2iIjdcj3tkimyU3tsmZAAAANAGe6XRH/wAAKzqdjWHCeWr8Rct83YosYot3nw9qhMU4YMRjfqJoa0I+sKHCjzMUXroon+AAAAA7AZ7rakf/AAArCB1/XWgPsvxbFr1rZeStFswVzredp3oDe1UYoB3lrLbRZSm8KHNs74YrhWXAChxzMLAAAACPQZrwSahBbJlMCGf//p4QAABWO9V33r6W3jweQbnRgB/JtC7XObbm/VDPFFXEJnXJZdhsvV8HyBLeKkkhS+OsWtFp///jThPHXhhHK1qmlypyTP0YZw314CmelfEOjh2FcY+c7gXTK98jp0TP3rweJKCm3AXqwTHdo/XKvWP1hdbqd+YTMvBxk8EhEuALrxsAAABCQZ8ORRUsI/8AABupfHFJgtmwFHX9QSFxk0FnaxkEnUMnimVbNCZn/naIwSwAlRUBMlPebOjfIEhcSc9KgUQENlUxAAAANwGfLXRH/wAAKx+eoKMkAoOGQhmJbYMGjmuEi9HYNg5B4m/8p1JKxetXa7Vay7pll7f5xIQAfMEAAABNAZ8vakf/AAAsWWz7lhEwk8k00ONTgNLp7/A0fHz2nEo3nsoAAH80vOlSt8RwuhyUdEEHumnQ0+NDu5lOr/sfAJHcuGDQdsZw+wIQmhAAAACAQZs0SahBbJlMCGf//p4QAABWK70vuABxtU34zDdVc/jpLdQb6TXl9S99EJdQlXZJWuU0lhJWEAmVfjfCZ9r9cOA/B5bL2RtkPe5iQadVoanX0klgMYRFcoJ2Qjj9A9uqu1aMf7Hy8Kds3xRplf7Iq+bjxX60Yl0VyLyqwiIq98AAAABUQZ9SRRUsI/8AABuoe8aAc/8fvNzGA8gRMS3rPHpDY4gG5I5KYXd+rOBjMUFWqnTWedxu+ZRK61El3owATrCOYVDl74qS/n/v9TmJbyN37l2qVHzBAAAAQQGfcXRH/wAALD+eku7wkoW/HDz0up65jvx6KVg43tJmkYwhKRUOxlXensU6g96UBZoAAnDI8Z/OMCH8g6HuSmLKAAAANgGfc2pH/wAALGMlazCLYxfav8wTWnHfWm2HzkzLFo+KLJy72yI20p68NV68Gh8NZZ7e5qHukgAAAH1Bm3hJqEFsmUwIZ//+nhAAAFh71wxJFtAEGNanb94FQ/mZnq24U4pLN9cHk9f65Th89Y168OgxHloSLqWDo4XUjv5KNXRCC1KULDQNrsc5lK96VuY7UbHl4h183nn3chBT5CmyK8ua/BC3iIeNW6k0JEvQY3CtW9Rb8oQs4wAAAExBn5ZFFSwj/wAAHFh/iB1lqwbeeT56Jehg9x1rm68ODM3I3pXUFhdiQedy3wwAS1CgNl/0imbsT9amvPkrMVihAWXUD/qzZfCgRtBAAAAAMAGftXRH/wAALFBQvg/wQRPjr0HIHHSzBsa2iKySf01y1XORgDupyvV9Whx+g+orYQAAADwBn7dqR/8AAC13M0VUiPahMivJ8KERSdW7CsWgXgsIItsoyil3iv/91UkeuHfrPQhN9AQFvS0ty3tBLuEAAAB1QZu8SahBbJlMCGf//p4QAABYa7963LqYZg0isnSm4OQtoZYTW5aB2fGZBRxZwXZ3WpRDDAQ8t3clQwUoS01772nkox9DiSxB3Yv6J1VaWLVwYGlfIXW9C28xB0Tb5EbQYZQV4zqsZAfhwt0t5A3fNomr8c2AAAAAPkGf2kUVLCP/AAAcTJBs6m+o4D6Gnxx3ei5PTZVSC532lmyC2M1uhYb2ZoPQGcAJUgV44bUO+4GAUqJYfj15AAAANgGf+XRH/wAALXqdhYtEMVXtIdASnbpmRM5okcl8DgpArzZHr5v2+P2FWdm8w26E/Si33tLNwAAAAEkBn/tqR/8AAC1Hj93Pj8uCdHBm4iQcm0wONfo1KjOY5+heUsYUuQeBsaVP8UKX+Ml7ACZhqAdtftMTh95gyDmonxt7uh4oks3BAAAAg0Gb4EmoQWyZTAhn//6eEAAAWtSFnlKQnRdD/KmhQVKoAH88lSkOa3vz45bmXkqPtF/QQazhx3xM9ok103G7iZc5L9hOb4wO9a9q6wcOTMnwJBKr4XPfaSqrFlKl4xz4kqzxhYc13U0ruseON4a4mhCaVmEv/9bJ9R5J6ODMskMhZ+jhAAAAQkGeHkUVLCP/AAAdBwIXqcCy+iGtBEbpVcZjOYoHiXVCP8dEoa2C6u6v+NpDX7sbxG5iPT01DGIs6GSm3TuQQiBNwAAAAE4Bnj10R/8AAC6anNCWsCSTOWQ0WtVK5wIjW0stmkYDyQoIgYe93hzVdX/dz5wIcABNXQp8B8kyvcVM6ORkDfktm99P2EHl9IhXA2UQs+AAAABVAZ4/akf/AAAugp0MqGoZWTNoMG8eZSFsM1rvxDpIxmwmdZ+7+ArOGB3yq6aRJwPT2antgAmREECI1fe/KczSqTzxuKfQf4tbuTt96r81tpzi6MRQQQAAAHpBmiRJqEFsmUwIZ//+nhAAAFz72js2AM4cgY9JsJYWTADPc/Vi1CzmcDi3tYRsaIQPwxC6NhUlC7Wq5kuRiosY+HyBD2MSMkqfL2AKCzUcy2XnKGpoPJU+L3sUDwY8W67TEPT+4EhDQcgr45pzT70h/PrQt9IiLlgl4wAAAFBBnkJFFSwj/wAAHbhyCL2FMGRyvhT56XTicbcky1fI+GY+kdnrk2fMKbGwAexUB5XKNdBREcFKbwAW29v/ORX+SAxAAKRm01uOfJyiT0kqSQAAAEYBnmF0R/8AAC5+vFNcwYb+9fJ0FLHmHPHO+0p1+BCi2Olt4JVvIxqeKAiDzo8x4AS1NBXBaWbiaHAAEBIrKL01uMS67AiYAAAAOgGeY2pH/wAAL7jcx+i8U6OnJCcxqhK4ay3xDA+Py1Jmr7EsPVtN+w3bD3QoJtzP0e2zXBSGkAjr0YUAAAB3QZpoSahBbJlMCGf//p4QAABdTsPyARq5m6ZxiCOeqoQKej8wbzoP/lNRa85Z356tPDqOEN2UapNYosBo0ESaz+GjDlWrLJiN9BZtfEUY5kyzWDet3jp/XkmFzaD735n66g1J/6ESETRgtEFzk1CLrpHKIVeaCQMAAABHQZ6GRRUsI/8AAB2snmznBJodLb8fpDOI0qwR2Vn0c23WCh1C6uF4y6tgAiDqSYsaB0UhpiWLEvpa/yp0cvVFgm3fy/VCSgkAAAA9AZ6ldEf/AAAvs47GkwjoZfg8J4d2EZ9uXu14lDId/bevV2emiyUTzc0RIWTe/aRvvLt4G9imECbaoADHgQAAAD8BnqdqR/8AAC+3GKPnegs4OJUm+YKJRxV8pjxzE37cyREncKWkXbAi/WkwWWkmswU3BPRqX5LPfXEriNuoUkAAAABpQZqsSahBbJlMCF///oywAABgKQzZs5c7VZOkh++O/lvKJVAOX9a9A12VmiVqjZW3WKhwqGEJXWLthK0tq/EJnnk41IAPa5iNApSd6KojSvHXztZY4s98S4r0W98pXfexWEhNQER06cOVAAAAT0GeykUVLCP/AAAeXJ5kgthj3vSIjfhJJZ4eFnTcz9AAwn1KyRfJkGDc6YotlD5o7LgTcQONjuPAl7opkyy6AEopuk0tGcH5bEQ1Zlv+Vk8AAABLAZ7pdEf/AAAxEX0P3ofiFocNRjO7lvRX1JEUOECoVXspFi1ft2Yu0kfgPHCZtnKOVL0+rmaopQq580v8PrMAJ0J/Y5GStEW8x43oAAAANwGe62pH/wAAMPcXtvZObBppFz5zSzqOdogoUogxaC06UTeH5RNUt64+0xIKOC+rIbFiMvbtpOAAAABvQZrwSahBbJlMCF///oywAABiwJqVYHoFWzWgQzkEW6Bf1Pkd5tgzypqSHILleW5YwfJ7GpE/+kyd4UoBAkjSinNNn/4OTtT3KzTwUcErk/wHLhRAZcB3+1zqUPb7LeW3/r7LRNZwpxlmp/7+e3cxAAAAW0GfDkUVLCP/AAAfFw0IfAHLBbFxXb1Cc9Cu3Z684fytMvMVfG5PclWs1+FeLUxXRZAfSrNAm1AxP6vqU7EBtmSMvqpD7fVP6zB07Rz8o+ASXXr1Ci6z9pU2KKEAAAA8AZ8tdEf/AAAyV1NtfWIbhXVu9NHXwd9BDCBDWtIjjdUIWOhpMqHOl03ypGLn8ZarTcGcoXIPCL61K+ofAAAAPgGfL2pH/wAAMk7vbILR24Zs8kDi+tqHQwc3jW+MO7dMAxwqWA9Rl9E4sPCNFSjEhQAebF88It6qGTddASvgAAAAfkGbNEmoQWyZTAhf//6MsAAAZS7GhmQ/YGAHJ5vnpT5bY7xvvF2Y4cgsr9a4cq752vxuOWRmNoLtuyNj2eoV+m86WM4nK8EjmNkuidzATcjVLYPQ/96+agpxMbMNzB0m1/A8ekYwFIgE36zbOXQJREk9IGic3crLNeZGIKGFrQAAAFJBn1JFFSwj/wAAH8h/iRmpar8S5TPmzuw8FXyAg9YQNegkTdLQ+ZGZYUxAKwAEyFflhaJ2VkrkxiwM7tLkU95s4EwBd8+Jcgqu0OE7GxZk0td1AAAAMAGfcXRH/wAAMlF9Gi0GH2Lo5hH1vzzvq3bo3p6dOct0bslYZKnxq6RtRsPhD9COZgAAADsBn3NqR/8AADN3OXb2LzUw17TsDNqA8KEyB+AdtTIXbBtOdhNBxgjSxKAD5F3mrnCjGOohxnG7Z0DFgAAAAHVBm3hJqEFsmUwIV//+OEAAAZE+TJaBEUi9PMU3H8DuWdBY88XdPKpOcvwjWDrRxIHaOS5DTptMXLpB4IcNZBcFNHpABwZhc2hyfz4LlLh5jTWeCg5DarjcpoeTlxeqlWn2vUInTRarB4N1VVHZVT5uecc0w+EAAABQQZ+WRRUsI/8AACC8eRvkwXOrx2y20mwqW4BZlY8AH+yif6OudC8fAJAaMCi83oAEPn5affos72A+rH91PtcA2vG1DQpgC6o5VcrPVUCcKf4AAABKAZ+1dEf/AAAzkXz4nS3ITuntKfs4jrdkHlTd1ugi8o2lf2pBdKh3dtAAiDoVVt7munseBU7g04ZM4q3H3cpz+mYgpWlYyJK883sAAABBAZ+3akf/AAA0t17EgDbP0f+1rcXcLgzZVtkaR55hdX8dC3RmDcE6LntL/LAXSNftrRU8bv7l6W+faxhBjAvL2fEAAAB2QZu8SahBbJlMCEf//eEAAAZEVmXigHydPvGoKTmsK6SmlPf46sYH9uQ6AmpFxF4x8no5ylYJGxco6XSJh3qpLl8Lx3nnaTUAaPuzcebot9VSGbNkReVOyrGqqDFmFW5mSY6ohwmKTAZZmfloNOU39SAqcMNjoAAAAEJBn9pFFSwj/wAAIbyFtVs0EzDH9balGEFeTlsNCacBc7CDTvaO7wtoVXrLU1Os1oKHDqF5wAC6TkvMkVW6C9rYKaEAAAA+AZ/5dEf/AAA00eJVEdctQ6U5Q+FsT85ih0mvLqu68gyCa8E8z8hGxqXmXYAQPMMfpKPxQmy8BG0UabHsI2AAAAA7AZ/7akf/AAA2ElEQoSmKnPOFX70K0+HmOJM3smAB/EhHm6UZWteqE4sTlbUzxJa1ob1j4WNikuRCHzEAAABPQZvgSahBbJlMCP/8hAAAGRmAbYArxo+cD/NoaQLRppVCxAyrDL+MDHZ6wf8WtuZkpkdcrt/aM811kngAT8tVbgJtrVtX+WCad6WEZNa7dwAAADxBnh5FFSwj/wAAIrx5LSTBdWN8lY1OlLKRyz4V9X9ANQeH131QBPP88EGCqAElSgT8H+xInz2yvIR2HrAAAABCAZ49dEf/AAA189VriiAZIgZTDa4bkx5vxtNWAx7ProUMfHoAwZqQn8Mn8++KqHlo74V7hQd6OMVnODbzpxlmZghYAAAANQGeP2pH/wAAN1I773MIucvr5Mn6jvywjac8jUSi52UjT6q5JHLoosALmz7TgaXFjOqfWwy5AAAAUkGaIUmoQWyZTAhH//3hAAAGbEg/pjm3crYeRdqi1+879HemwPMdSiIrnD/VlH/1LIFrucuJePUANEjaANcbn+grv6maJ0PETi5sL0jxq9I782YAAABQQZpFSeEKUmUwIR/94QAABpRXFtSvdCDvxlUITDnJ3xdsFBClnQ8tA48QNhOgevd0p/FIviMz5kALeGlkAfARa1RCvv9PyOrU5shw9KEpFmUAAABBQZ5jRTRMI/8AACO+KMVPA4bFJGmT8qlbaDy59BRr2XKlaOk4QMXEbze9gGLM3v9ZbsX27EWOGKrYl8e4gFbgA1oAAAAsAZ6CdEf/AAA3UeIaLQIo7Rw51EdG8B5AIT+RGmWrx6sMzFPQt1jT8tDZKSEAAAArAZ6Eakf/AAA4kLMLmoQiJTdb6Bso+twfubd2xSYFZDh9NayHbnwB7sQKCQAAAF9BmolJqEFomUwIR//94QAABpRXTDvo4ARrv0H9/u3DAlSXWm53szZ6q4iMIX/tFkjbclAC6AksXFWqtj7Wt38EdK6Sdgrprw9idBZ2KidevYMjydqlmJko8np1+GWLJwAAAEhBnqdFESwj/wAAI7qR7TRgbCuZqydoyCwfYHh3yG7YtoTDk3HjYGy1tafuf6nCvuwAF1FxjW4xbxH72/bsfB5VAV4o6jyAekEAAAA3AZ7GdEf/AAA4r0Y2MWCHlQNWfBiDDLyCYpBJuoW0gBK+ZATxuf1avcUAHyBhQb5aOumXsHA9IAAAADoBnshqR/8AADiQ05HcTbr/7yt4TR3Qmf5q4YDcUnqzwDi6ip32FzITBrCgAtTnmrnqdyBb/oBUoGzAAAAAPkGazEmoQWyZTAj//IQAABnfRROXsVAthEepTGcksZkaJFxNQHkwHiYLACwCRqglkG8gOEBJ40QBElLNphi5AAAAJkGe60I//wAAOJ/xMLgnHq9jQzger6A5B7G2/FOTK3guRk8gQyVgAAAANgGfCmkR/wAAOJRosErQk0mNPwQzu/cSO6Y4xlDwokZJBLGIJE1gAJauBeT0qwmaM1i9nn8MWAAADfRtb292AAAAbG12aGQAAAAAAAAA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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 8<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 64<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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type=\"video/mp4\" />\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos = np.array(env.videos)\n",
    "n_videos = 3\n",
    "\n",
    "idxs = np.linspace(0, len(videos) - 1, n_videos).astype(int)\n",
    "videos = videos[idxs,:]\n",
    "\n",
    "strm = ''\n",
    "for video_path, meta_path in videos:\n",
    "    video = io.open(video_path, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    \n",
    "    with open(meta_path) as data_file:    \n",
    "        meta = json.load(data_file)\n",
    "\n",
    "    html_tag = \"\"\"\n",
    "    <h2>{0}<h2/>\n",
    "    <video width=\"960\" height=\"540\" controls>\n",
    "        <source src=\"data:video/mp4;base64,{1}\" type=\"video/mp4\" />\n",
    "    </video>\"\"\"\n",
    "    strm += html_tag.format('Episode ' + str(meta['episode_id']), encoded.decode('ascii'))\n",
    "HTML(data=strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:51:55,037] Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/tmpbj36uk9x')\n"
     ]
    }
   ],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:57:18,333] [CartPole-v0] Uploading 100 episodes of training data\n",
      "[2017-04-26 19:57:18,960] [CartPole-v0] Uploading videos of 5 training episodes (59482 bytes)\n",
      "[2017-04-26 19:57:19,343] [CartPole-v0] Creating evaluation object from /tmp/tmpbj36uk9x with learning curve and training video\n",
      "[2017-04-26 19:57:19,617] \n",
      "****************************************************\n",
      "You successfully uploaded your evaluation on CartPole-v0 to\n",
      "OpenAI Gym! You can find it at:\n",
      "\n",
      "    https://gym.openai.com/evaluations/eval_9GmUYkMTDO639hU9KdeCQ\n",
      "\n",
      "****************************************************\n"
     ]
    }
   ],
   "source": [
    "gym.upload(mdir, api_key='<YOUR API KEY>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Your turn\n",
    "\n",
    "Nice, the agent is good, but it could use some help. It seems the agent is learning, but perhaps not enough. Try modifying the neural network... Maybe more layers? Larger hidden layers?? Try things out, it's now your turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def action_selection(state, model, episode, n_episodes):\n",
    "    epsilon = 0.99 if episode < n_episodes//4 else 0.33 if episode < n_episodes//2 else 0.\n",
    "    values = model.predict(state.reshape(1, 4))[0]\n",
    "    if np.random.random() < epsilon:\n",
    "        action = np.random.randint(len(values))\n",
    "    else:\n",
    "        action = np.argmax(values)\n",
    "    return action, epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def neuro_q_learning(env, gamma = 0.99):\n",
    "    nS = env.observation_space.shape[0]\n",
    "    nA = env.env.action_space.n\n",
    "    \n",
    "    # memory bank\n",
    "    memory_bank = deque()\n",
    "    memory_bank_size = 100000\n",
    "    \n",
    "    # function approximator\n",
    "    #### THIS IS A WEAK NEURAL NETWORK\n",
    "    #### CAN YOU HELP THE AGENT LEARN MORE\n",
    "    #### COMPLEX OBSERVATIONS?????\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_dim=nS, activation='relu'))\n",
    "    model.add(Dense(nA, activation='linear'))\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "\n",
    "    # constant values\n",
    "    n_episodes = 50000\n",
    "    batch_size = 256\n",
    "    training_frequency = 20\n",
    "    \n",
    "    # for statistics\n",
    "    epsilons = []\n",
    "    states = []\n",
    "    actions = []\n",
    "    \n",
    "    # interactions\n",
    "    for episode in range(n_episodes):\n",
    "        state = env.reset()\n",
    "        done = False\n",
    "        \n",
    "        # each episode\n",
    "        while not done:\n",
    "            states.append(state)\n",
    "            \n",
    "            # select action\n",
    "            action, epsilon = action_selection(state, model, episode, n_episodes)\n",
    "            epsilons.append(epsilon)\n",
    "            actions.append(action)\n",
    "            \n",
    "            # save history in memory bank\n",
    "            nstate, reward, done, info = env.step(action)\n",
    "            memory_bank.append((state, action, reward, nstate, done))\n",
    "            if len(memory_bank) > memory_bank_size:\n",
    "                memory_bank.popleft()\n",
    "            \n",
    "            # iterate to next state\n",
    "            state = nstate\n",
    "\n",
    "        # only every few episodes enter training and update neural network weights\n",
    "        if episode % training_frequency == 0 and len(memory_bank) == memory_bank_size:\n",
    "            \n",
    "            # randomly select batches of samples from the history\n",
    "            # for training to prevent values spiking due to high \n",
    "            # correlation of sequential values\n",
    "            minibatch = np.array(random.sample(memory_bank, batch_size))\n",
    "\n",
    "            # extract values by type from the minibatch\n",
    "            state_batch = np.array(minibatch[:,0].tolist())\n",
    "            action_batch = np.array(minibatch[:,1].tolist())\n",
    "            rewards_batch = np.array(minibatch[:,2].tolist())\n",
    "            state_prime_batch = np.array(minibatch[:,3].tolist())\n",
    "            is_terminal_batch = np.array(minibatch[:,4].tolist())\n",
    "\n",
    "            # use the current neural network to predict \n",
    "            # current state values and next state values\n",
    "            state_value_batch = model.predict(state_batch)\n",
    "            next_state_value_batch = model.predict(state_prime_batch)\n",
    "\n",
    "            # update the state values given the batch\n",
    "            for i in range(len(minibatch)):\n",
    "                if is_terminal_batch[i]:\n",
    "                    state_value_batch[i, action_batch[i]] = rewards_batch[i]\n",
    "                else:\n",
    "                    state_value_batch[i, action_batch[i]] = rewards_batch[i] + gamma * np.max(next_state_value_batch[i])\n",
    "            \n",
    "            # update the neural network weights\n",
    "            model.train_on_batch(state_batch, state_value_batch)\n",
    "\n",
    "    return model, (epsilons, states, actions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's test thing puppy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 19:57:54,544] Making new env: CartPole-v0\n",
      "[2017-04-26 19:57:54,609] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000000.mp4\n",
      "[2017-04-26 19:57:54,893] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000001.mp4\n",
      "[2017-04-26 19:57:55,191] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000008.mp4\n",
      "[2017-04-26 19:57:55,497] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000027.mp4\n",
      "[2017-04-26 19:57:56,004] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000064.mp4\n",
      "[2017-04-26 19:57:56,769] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000125.mp4\n",
      "[2017-04-26 19:57:57,577] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000216.mp4\n",
      "[2017-04-26 19:57:58,734] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000343.mp4\n",
      "[2017-04-26 19:58:00,237] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000512.mp4\n",
      "[2017-04-26 19:58:02,469] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video000729.mp4\n",
      "[2017-04-26 19:58:04,339] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video001000.mp4\n",
      "[2017-04-26 19:58:11,961] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video002000.mp4\n",
      "[2017-04-26 19:58:19,229] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video003000.mp4\n",
      "[2017-04-26 19:58:26,374] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video004000.mp4\n",
      "[2017-04-26 19:58:34,311] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video005000.mp4\n",
      "[2017-04-26 19:58:41,346] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video006000.mp4\n",
      "[2017-04-26 19:58:48,594] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video007000.mp4\n",
      "[2017-04-26 19:58:55,895] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video008000.mp4\n",
      "[2017-04-26 19:59:03,698] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video009000.mp4\n",
      "[2017-04-26 19:59:10,119] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video010000.mp4\n",
      "[2017-04-26 19:59:18,031] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video011000.mp4\n",
      "[2017-04-26 19:59:25,564] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video012000.mp4\n",
      "[2017-04-26 19:59:32,222] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video013000.mp4\n",
      "[2017-04-26 19:59:36,582] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video014000.mp4\n",
      "[2017-04-26 19:59:41,067] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video015000.mp4\n",
      "[2017-04-26 19:59:45,155] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video016000.mp4\n",
      "[2017-04-26 19:59:50,109] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video017000.mp4\n",
      "[2017-04-26 20:00:00,245] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video018000.mp4\n",
      "[2017-04-26 20:00:13,133] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video019000.mp4\n",
      "[2017-04-26 20:00:22,580] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video020000.mp4\n",
      "[2017-04-26 20:00:27,651] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video021000.mp4\n",
      "[2017-04-26 20:00:35,296] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video022000.mp4\n",
      "[2017-04-26 20:00:41,948] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video023000.mp4\n",
      "[2017-04-26 20:00:50,118] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video024000.mp4\n",
      "[2017-04-26 20:01:02,535] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video025000.mp4\n",
      "[2017-04-26 20:01:16,255] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video026000.mp4\n",
      "[2017-04-26 20:01:28,691] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video027000.mp4\n",
      "[2017-04-26 20:01:42,927] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video028000.mp4\n",
      "[2017-04-26 20:01:57,091] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video029000.mp4\n",
      "[2017-04-26 20:02:12,337] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video030000.mp4\n",
      "[2017-04-26 20:02:28,220] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video031000.mp4\n",
      "[2017-04-26 20:02:43,692] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video032000.mp4\n",
      "[2017-04-26 20:03:03,988] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video033000.mp4\n",
      "[2017-04-26 20:03:22,602] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video034000.mp4\n",
      "[2017-04-26 20:03:42,160] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video035000.mp4\n",
      "[2017-04-26 20:04:02,483] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video036000.mp4\n",
      "[2017-04-26 20:04:20,499] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video037000.mp4\n",
      "[2017-04-26 20:04:36,198] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video038000.mp4\n",
      "[2017-04-26 20:04:50,712] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video039000.mp4\n",
      "[2017-04-26 20:05:02,977] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video040000.mp4\n",
      "[2017-04-26 20:05:16,803] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video041000.mp4\n",
      "[2017-04-26 20:05:32,540] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video042000.mp4\n",
      "[2017-04-26 20:05:47,084] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video043000.mp4\n",
      "[2017-04-26 20:05:59,625] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video044000.mp4\n",
      "[2017-04-26 20:06:17,990] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video045000.mp4\n",
      "[2017-04-26 20:06:44,408] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video046000.mp4\n",
      "[2017-04-26 20:07:15,308] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video047000.mp4\n",
      "[2017-04-26 20:07:28,618] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video048000.mp4\n",
      "[2017-04-26 20:07:54,397] Starting new video recorder writing to /tmp/tmpa2d2gyxr/openaigym.video.2.1418.video049000.mp4\n"
     ]
    }
   ],
   "source": [
    "mdir = tempfile.mkdtemp()\n",
    "env = gym.make('CartPole-v0')\n",
    "env = wrappers.Monitor(env, mdir, force=True)\n",
    "\n",
    "model, stats = neuro_q_learning(env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Wake upp!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h2>Episode 0<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 10000<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source src=\"data:video/mp4;base64,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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 29000<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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tw3BNAj7rJyuXDULRo1qF2K1Fh5HclryzJhI4TtAKxnPAhFgaAJIII6AOKqAV5WBHIGmmvFhov4pFtSBAAAAKwGepXRH/wAAJKv4X1VyAfMtt+Zou6QALQhb2Wbgaxn7hMcJSeRv5GN9QIEAAAA7AZ6nakf/AAAkvwQnEsr+fEvjohgAuolux9Wr2ERuv+YeK8+1T4vGQAQekrmrGE3rXLeIsjICBLqKwsAAAABeQZqsSahBbJlMCGf//p4QAABHRFoiXTlymH0EqvAGzDL+Y8MYasl5c/d6yr7yTkh9DjS8ezmhXn/RV2Mamt9fBikGU8XDaOUFUELPNzEwa43z3Ve8TwJiajZDUOn5YAAAACpBnspFFSwj/wAAF0NHtw4AfDGScXrT5Z/J86QBt2WaR/DMcMPrZwvbZTcAAAArAZ7pdEf/AAAkwxdoqay4vl7AAuer3sbPp46mYdnAHmzHOIOi6Vk0bmpFgAAAADMBnutqR/8AACS/BCfbas2xOP2YIAFz+7fp983SzbJSOYvC0hWDKPSwUV7ebySNWa+kq8AAAABfQZrwSahBbJlMCGf//p4QAABHZI4WFltwgelM3pJZKFTiQQH3Rb3Pu9gpRTegE57Hr5Vw9lcB5I2TbFIsz+j5cNGlqROZ4r0HSJ7mFN4lJmqYwuFn1lKTidf029728GEAAABDQZ8ORRUsI/8AABdDR/txkFEXio5TIkAVCcGWjGCXZH1dp9w0+z0f2uu52VF6/DjDeOhaPepYHlk8P/hABW5pbximwQAAAEgBny10R/8AACTDF2Gon7a9ZSBMUIAFp/b31NrbGfOMQxTJZIRioAcAcoxSYDHogV7rQtgVwbC2+XvM9wIWzSX3anPtEB9WkWEAAAA5AZ8vakf/AAAklHakL3+Sa48J8lUfJK1nVyO2GACWPNvUqw1tFZn03DPLTVwrILeo4zd7XX/MsCvAAAAAc0GbNEmoQWyZTAhn//6eEAAAR0RabE28rpFeIOAf//axBIw8B0AgxJ7UfhO3yExdybLyDB5+97yWWSMaG0vtBetjel8Z+KJ1mlD1+mxvEylTWh37S3W/NA2rYn5Zi2aj1T8zlDvR+UmmOsR9o1+7bPQVM4AAAABUQZ9SRRUsI/8AABdDR86oP9QA03qpN7s7SsPbh9mlqlmJJ0nHpNTX/V12JBxZ2XoNSvCAx7vv8g6PII3SmGZrJ96K4dLzUXa7W96tovtL29dgJr3BAAAAIgGfcXRH/wAAJKv4XC0bFGwTz0EiEoVLLKqaiAN+H+ZrjZQAAABIAZ9zakf/AAAksiW+3ZHgm6D4gAXDCHuAX/0k4XJsxZiaxoUICIFCa8cKRKzJCatSbgQvTQ4mEDTmSvOjJ+nvs1rS95mWs6bAAAAAdEGbeEmoQWyZTAhn//6eEAAAR0OQb4AHFpcLSIqFqt7UdbC7Wy2iWFa/aUDht3gt4SWVrXv7k599ZWeskSofCPSQDCvVQUgqxmrcuM0IF4Wks1eacvZ57j8zkAUhU0vqqMMvGQ4kVJIt6jxAI/7OvT5SPP9ZAAAASEGflkUVLCP/AAAXQ0iDMZkNABLIKoguvKtPTC0KFKgcbQOCUfdPJPFEHe2fxVpdHROGpxAXGkogWxs5oXHhgs+5lOIXzEnCLAAAAEMBn7V0R/8AACXDEeb36rWlLb5YecqFOSRjDfAE//VTnO8d/1imyRtW/X1waPAN3HYMv9ulVP33WnD77sQwXjaYFWFhAAAANwGft2pH/wAAJce3o8gyd+YAFp/b9xnRbWx2GwKTgtKVUDnrM8QQH/qFzMg7gh7KKFso1+XJRlEAAACIQZu8SahBbJlMCGf//p4QAABJQ82FaiEJzEkfIAjU/6M6ol+VFrMv0A/SU/2R88T+vw2iyRbJT1t11PrzIFbh7AZBAo/9Ig8gVukhHciz0bPR6bOJ+r9J93lEu245VhQA5u77GlomNY450xKTgUP66YxLZCbW7XeeorV1aqbHSWU6o98uWB/SPAAAAEZBn9pFFSwj/wAAF+vkifYQoUHNbh7n+uHEiHr/DTNdW+nYBlfruzVn/poqgA+/dGA/wnhEfX7JhKqgxFGcRVw3OkBE53uBAAAAOwGf+XRH/wAAJav0wai86hmte0eL3Faxm5/43BywbyFQjLaXUXwxXPDZsACDwXW4bIth2LjWia3mcOCAAAAANwGf+2pH/wAAJa5eM4HMGU1a1fpITwJVvVphSGFKpuVfn3isvwbNNAB/MLf0JnRffujQdcfMyLEAAACQQZvgSahBbJlMCGf//p4QAABJUOnZoACvPRjIli1AgBPY38ay/P7zzSubBet7TBagO8xeVLiWfR/FvzyTXd7QimT/xwT7flNBIRl0YnzruC7kFMmiTadwvz2WgIt+82ZwAJBdpSRxKsgChJPyTSsOBWvCeOOOD5lun+80zn173khuVIgxSaF4+XUPk0WY8CHhAAAAPEGeHkUVLCP/AAAX7XbWuFEMoQALB8HUk4497LyVrKlo5pRefMoGCWavVxOz0RbCW9BjrXvmMmF4YYeRYAAAADcBnj10R/8AACWp56h3cBIA8uiJxst6aONDoanHyhVfbzRUaCBgKgtEgAB97/N7Y9fgxP3bKX+AAAAALgGeP2pH/wAAJa5dneahZWTNIXZKouWVqlTkW/D2svIbF3bHgKVaSbyDia4vWpEAAABqQZokSahBbJlMCF///oywAABKEJzRQb/WxT1WsxDZFErQOwAEm0siJW+dUBE4AgsTHwtBEoDBDtsJ8TZH8eFJk+xSiN219AApC5DH0464q/tW8z6XJ0l4ox5+N9G1JA/mAsqTW5NtrzuS+gAAAElBnkJFFSwj/wAAF9c/+4WIy/y7/x505Zb6d352VsVT841WV0oITKNvEyJiv+dniEvO8paY/ex4APvXyE1SzKKmHPQ8tmAXibUhAAAALQGeYXRH/wAAJam48hNMiEV5rCLNU/PU1O6HcLetL2iHwoE66bCYXDqoUwzWFgAAAC0BnmNqR/8AACW9g4HKAPsnBQ+aCei61EWnQ2s46Pfcjw6p4l7AAJiYxwPoNUEAAAB8QZpoSahBbJlMCF///oywAABKAV/VzABxSlKZIkgjr809X/AtFMQm5WV2BEXPETsRrfF16OxKhbbeP1cvGO539nN1kN0P7uz6ejjFGhKPAhAs6ztRwzDTsbNxcdOFVHIsdDxLRsQ2/SrRRfu5Fcr9rGD3l5VUGmCSvsrpqQAAAFxBnoZFFSwj/wAAF+ibgrVvBG0/f/b3aqzRm8oBhVHgKn7QfTN0cZUxwnDcuKHn/hKQ1z4EbYAIa57Rq1RQnZ0YG/Bo7E9doN0Lt7Z2XU1l4h2ygG2BeX41sVbnWQAAAC4BnqV0R/8AACXAmowjjEeiT6mIr/zAvmTshyDj+dB3I4Q15H1xK4j5gAjGV1BBAAAANAGep2pH/wAAJb1lmxrTcwYjqd8VjrZ9+pDyulHE9h+n3Ip2Ta8ZZOz52+HZyoIkzSKelpwAAACJQZqsSahBbJlMCF///oywAABMEKm0pIgAcLPa/CfHmCCZS9FXNE4wa5x75l4qx4mTb2HC8vis7sCE+U6JjGZE56pAqgsTH5WC/1nbgSO+znbOx3QIW+q4qQu9JDpw208mWKjYxP67RoloPs+JRN0B7Nd3AeF+KpcwrSaNdygEWI17AJ4ahWXOdnAAAABbQZ7KRRUsI/8AABiNezgMktGb2q1JrvXQTFPO9nZBzMbsRMY8tPDfUMd/9OEITOU7FeFF0jVAALPd0cY52Xavi2wATldMrHv6/65PqBTTxWzJEm/50r1ikffdYQAAAEABnul0R/8AACbC1Hd2xwYQaYdACyMsc2y4NllOjDcRCNiKCfA4In0blYxurfvJP3FOKqmR7jGOlGYwWOYoX3WAAAAAOAGe62pH/wAAJr0mflgga8eUoys4sLhSVjTE135d/AWTXt1JX+tbR3w+E6Cw45fTzZvhqzuGeFpwAAAAtEGa8EmoQWyZTAhX//44QAABJTf7r4CwAcbmgxGRnOsX3lArIobqaaSXRuJ9JNGTqA+3MLxFyPfuT/ec2VcOGATA5TPiqRnCOYspD28I/7rEKIUJcZua59QuJkFNf+pTp0u3OzzlS4EhZ71oRZUgGC3SD7Bbu766nYDQBGLehAoPlEWjioZZ5B/eZ56KbB+3TZN1mAtw9antPgQQvz3jcwxKgad4F85PN+Y0xSdIDujqtU4IgQAAAEtBnw5FFSwj/wAAGDLRq5wE0KSSiah/v5EjPwI4Vziuc3N7YWaUpr12chkcrSefIzQkwS7lFW/tPlJRkZ1m5d2qKka/ODJmOxNMbusAAABEAZ8tdEf/AAAmo2PgAA7XfVX4dI35eXolU9FH9JogYvQqixzb9nrH0B5ZahvZl6TI1JwAIGOKw+XzV0Sf3hFbO1sOOnEAAAA2AZ8vakf/AAAmeS9gdSPZ7YDMlms5FTooReHgBdDic/STglIZKATA/anV4Z9hIXuINao5sHkGAAAAgkGbNEmoQWyZTAj//IQAABHLblip8V+PXiRTfi/4Up4iww+BP7ncvPFAluMr1YALn0iymy+exRirqVzGHHl1kS3I+DNhvvc4D3PEiWOFktTabTswrLy96dhwMpa+8thPZ51V1lTtZk98YD2Ar+1ffO0+ovaPu51xhlPSg6YJZ+rXjcwAAABuQZ9SRRUsI/8AABkf6/OHktT24WcTVvZiLTHrYvROvKrIYLy6NHvATDZ73w1MbGoD97v4P7AOpTwUe+UfzN52Y3xeBBLOYIG42rl4mPD3ohQAjtlUm0OUoQlrx0gYx6di8St/+FUra2hkxgOyHgUAAAA3AZ9xdEf/AAAn3hwk9mExvAOlJogYP05VTWuP5AnvZYdRr2wY1GWUpc2BuP2UZrzrzvmnopJDpwAAAEMBn3NqR/8AACfZbQiYkAKxB+qSklwNIdvC9pG75LU9+PU775lYOHzn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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 49000<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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type=\"video/mp4\" />\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos = np.array(env.videos)\n",
    "n_videos = 4\n",
    "\n",
    "idxs = np.linspace(0, len(videos) - 1, n_videos).astype(int)\n",
    "videos = videos[idxs,:]\n",
    "\n",
    "strm = ''\n",
    "for video_path, meta_path in videos:\n",
    "    video = io.open(video_path, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    \n",
    "    with open(meta_path) as data_file:    \n",
    "        meta = json.load(data_file)\n",
    "\n",
    "    html_tag = \"\"\"\n",
    "    <h2>{0}<h2/>\n",
    "    <video width=\"960\" height=\"540\" controls>\n",
    "        <source src=\"data:video/mp4;base64,{1}\" type=\"video/mp4\" />\n",
    "    </video>\"\"\"\n",
    "    strm += html_tag.format('Episode ' + str(meta['episode_id']), encoded.decode('ascii'))\n",
    "HTML(data=strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 20:08:54,315] Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/tmpa2d2gyxr')\n"
     ]
    }
   ],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 20:08:54,359] [CartPole-v0] Uploading 50000 episodes of training data\n",
      "[2017-04-26 20:08:58,186] [CartPole-v0] Uploading videos of 59 training episodes (218203 bytes)\n",
      "[2017-04-26 20:08:58,714] [CartPole-v0] Creating evaluation object from /tmp/tmpa2d2gyxr with learning curve and training video\n",
      "[2017-04-26 20:08:58,989] \n",
      "****************************************************\n",
      "You successfully uploaded your evaluation on CartPole-v0 to\n",
      "OpenAI Gym! You can find it at:\n",
      "\n",
      "    https://gym.openai.com/evaluations/eval_ghlZ4ETiOQpKCHSm7cg\n",
      "\n",
      "****************************************************\n"
     ]
    }
   ],
   "source": [
    "gym.upload(mdir, api_key='<YOUR API KEY>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You happy with it????? Are you sure????\n",
    "\n",
    "If you have any issues, check out my solution. But don't do befere you spend a good deal of time on this one.\n",
    "\n",
    "Let's look into the stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "epsilons, states, actions = stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7facb082b1d0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+LSKORsTuuu1XMvO+uv99wNPnWdPaerm3fbYxeu0EPtFYX+y5epjqiHLKMOemd1s/o/++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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad06ce7ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(epsilons)), epsilons, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NbfV/k9y/X2e1NBwFfG+xBstnFmukBVvU4/Ey57F4oQPmeHR+Dn9+Po2WaphMAccMra8E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad06bd3f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bins = np.histogram(actions, bins=3)\n",
    "width = 0.7 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "How about running your fully-trained greedy agent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 20:09:02,440] Making new env: CartPole-v0\n",
      "[2017-04-26 20:09:02,453] Starting new video recorder writing to /tmp/tmprkc4htq8/openaigym.video.3.1418.video000000.mp4\n",
      "[2017-04-26 20:09:04,081] Starting new video recorder writing to /tmp/tmprkc4htq8/openaigym.video.3.1418.video000001.mp4\n",
      "[2017-04-26 20:09:05,973] Starting new video recorder writing to /tmp/tmprkc4htq8/openaigym.video.3.1418.video000008.mp4\n",
      "[2017-04-26 20:09:08,599] Starting new video recorder writing to /tmp/tmprkc4htq8/openaigym.video.3.1418.video000027.mp4\n",
      "[2017-04-26 20:09:12,596] Starting new video recorder writing to /tmp/tmprkc4htq8/openaigym.video.3.1418.video000064.mp4\n"
     ]
    }
   ],
   "source": [
    "mdir = tempfile.mkdtemp()\n",
    "env = gym.make('CartPole-v0')\n",
    "env = wrappers.Monitor(env, mdir, force=True)\n",
    "\n",
    "for episode in range(100):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = np.argmax(model.predict(state.reshape(1, 4))[0])\n",
    "        nstate, reward, done, info = env.step(action)\n",
    "        state = nstate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h2>Episode 0<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 8<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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type=\"video/mp4\" />\n",
       "    </video>\n",
       "    <h2>Episode 64<h2/>\n",
       "    <video width=\"960\" height=\"540\" controls>\n",
       "        <source 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awNLezwPDlLIUd4E1nd96ocAI/hYYRReqnQzGXjHAAAATkGeY0UVLCP/AAAXQ0iG4Y1hADa90cYXfSTccZwLiqxgPydhOWKjAKC+MBJ4wrnGSwFOZfLNvUwU48n8LtUCrLkUAZP7PpE0SojNyoAkwAAAADYBnoJ0R/8AACTVIQAcbCUADwRZ8xGw5S81GlsWdH5O0jL6w+dhTjGbafgDHQYVKoORhy+SU2EAAAAqAZ6Eakf/AAAkxwgxip5uuB51QgSNCiGhHMwzEPPTD/miP9jZAQRiID3BAAAAdEGaiUmoQWyZTAhn//6eEAAAR4LJGCjVABFr4AQfi5VScXxBe3LcXrxBgTzuFjs291O8i2lCdsp5QOvB4pGlJ/07e9sOmq/vfJliFmNw6Y/7zkNnksbl8Phb+23MhXelk8p5imnNsL07RcrbTT+NK+XEq1chAAAAP0Gep0UVLCP/AAAXS1t+lKF79F2P5SQVtvwKrBTPCzOwhcx1f6lBPUFTPLvIVvujuYUqoXziFCa1aHP6bPphtQAAACsBnsZ0R/8AACTAmqGeehsPyr42VBlmvQwDV8miF+rt5UKDGpw1MejrftSAAAAAMAGeyGpH/wAAJIQFtsuAalrqgLGVq3YWinT0X3I0492QCpO7Qn51ov/NxQbx1NHakAAAAGNBms1JqEFsmUwIZ//+nhAAAElQ5iXAqbPhtHcIAMsD6BTShAeCXMhxkaKg+1tNvC/aWNWfU3PoIMIAsRCUcRoBvCqSt6eKbQ8WuXuDakzetRXKQHRyT7dRmhWhnDNtymyHoNsAAABHQZ7rRRUsI/8AABff7B6jLDfq0bcGZJSNaQZJEana/nCMF1xsD7pDXWN8IALoCTVJ4G0aHygl+BO6VfEXzS6GCl5+O9WZLxYAAAA1AZ8KdEf/AAAl1SEAbbhdGzSYzzmnjQ8EBxsEFS4e3iPg2orV7pHMSEnDhb26ji/0IjxAPcAAAAAtAZ8Makf/AAAlrl4zf7w5+0J8dux8oc9dOGJ+7M/CNsl23usUxEv//RqTnTixAAAAXkGbEUmoQWyZTAhn//6eEAAASWiTzLh1gAsBuCxMJDrIOcXdYCY2zpQQscaBOnWdc+QqqZvE/LAHkdrGp/gE2RRqPMwjFRG+qMDJOEqzWGcTZqZ9DZGTUY+IS4AmbzsAAAAyQZ8vRRUsI/8AABff6+i0PNOX5CzHwb/X1jHuGe+vsrJjblb6JQVwWS3eItUvYFQez4EAAAApAZ9OdEf/AAAlwuzha/pnAGlxzP9TMFSbc2qaromXIa3rZ3Z2anSt2fAAAAAmAZ9Qakf/AAAlrl4zgXlElCxUOb7oq5B+f+Msj02VkZKNCDyIxSYAAAA9QZtVSahBbJlMCGf//p4QAABJY8DweT/3wwFyXVFwi2+0FAt/7IXlTDlNEHvbZmOMl8xLSRATQdY47gqY/QAAADlBn3NFFSwj/wAAF+ibYuSiv3yFZbbyJXg14eaGznm6bXwABI/GP0WMLVJYENokaZ6yB0hrywr4yDAAAAAoAZ+SdEf/AAAlwJnd55/PD+E1AEjOCgH2Vbo+D9WvO9KFjzjBnIxs+AAAACEBn5RqR/8AACWuXuJcNhKpkb6tYOh6fHJqI1E4HuSGpBkAAABrQZuZSahBbJlMCGf//p4QAABJZAQj/UBVAEVyl55Dvgdk27GnnFJRdkEPxKB4yEEjt25y+6uw7/CFJiPHxakkL0KyS2f0Ro444+wfgHrGbTTdGmCR39sN8f2TsFqGbNSZpx8VmMAiNiuX/WgAAABCQZ+3RRUsI/8AABff7C9X5VNgcbo1Kx7yp0RrEl47RF8RACeqNktgALU8V+X2rytSnylvnLrBgCoYugRpnVpi/Jz5AAAAOAGf1nRH/wAAJaNpaKE5gADiZ4+8UGi8tVMmEVRHR1wfKN2IelRbviTgM2Ae38fYCGomds+n93WBAAAAJQGf2GpH/wAAJa5gBQtLm7Ymmwx41zthPtw0sjMj05GWGruTlnwAAABKQZvdSahBbJlMCGf//p4QAABLQ81wrzMykxGwHuSC/OJLnh4Ttk3SyDxPaHxDIvN8Vlia77ee3p7/QA3Q605yvXp3rFg0XWmZNlUAAABBQZ/7RRUsI/8AABiJfMNgIFtWGVFUPElANQBRmgAbR6JAdoWmGmFbdr09AfmYhdC2v0fNs2+PXNPnN7y5fpXN2fAAAAAkAZ4adEf/AAAlqeru0S5JMyZT0qH8cYJENoVUHYil0Z+qQGPdAAAAJgGeHGpH/wAAJr71PhpkP2gGlcDj0UCHt3SVQLpp40b3APHd6k7hAAAAT0GaAUmoQWyZTAhn//6eEAAAS0PUKbs2VnpAHKN4K8DhE8Ki1IPrm1+pTC4Z0o/4lDE/X6uwNolS+SoW7NgHgCNjwSiK4x34dxxHYCogxoAAAAAoQZ4/RRUsI/8AABiIm26m5g7vuHc6XIbEES2EJeIP1NTa6jHpvTSO4AAAACABnl50R/8AACbAmd3pXpFlEfqB0DMW4YkKJakE7/kbKwAAACABnkBqR/8AACauXj2Clc8zzbZAZScgzYDfx2daJfLBHwAAAFxBmkVJqEFsmUwIZ//+nhAAAEtDzYcunB/svmxK3P9KZ8mAYrEghQCycYcy4o6UopvQA3qQS3z0O/xkhsK4y4Lvh/oi4YgAVfeemhIdlAyT8YoobSExHFfZYwherQAAADxBnmNFFSwj/wAAGH/uq8AVu7znKFRim3SzQzQZKXa1z1aYKa5OsslkcFoSK2XWYjFSFQY9WcPji1ZFGvAAAAAkAZ6CdEf/AAAmqenHE4V0NuStf6oorGd31FK2ytckrAYcu22hAAAAJgGehGpH/wAAJq5e0LPLZURF2ovwF800tNCS6E6+ZD2Lr0QwuR3DAAAAbUGaiUmoQWyZTAhn//6eEAAAS0PNrCbeToaTFuuC65xuEMj/LwAGizAos49xuHa6ezmLxxLjDjfNBNTuoTx60amURUq2pFLuHmEwt34Kbul1SteewlGrlKiRd8Yto5inN4RoqBm5tGeMj0ExIPkAAABBQZ6nRRUsI/8AABh/7C9TppmuukZW4ZPLztG+qGDQjIM9mklkZyfwj7MAuAd2fAJWrNMxBHnTTtPDp3ujRrsaaLEAAAAnAZ7GdEf/AAAmwJkyfLM+DyS8BGMqf5ieVOnwYzdqWGpwSbXn7LRYAAAAKAGeyGpH/wAAJq5f+zgRiffWr0mOngCRWko+WdcwOyyFn5PgkYEbw7gAAABPQZrNSahBbJlMCGf//p4QAABNQ81wrzMzmAikLp8XBmyLOTaoAbryCUKUrfqUANBSGIMflAKrclVxJre0iLsJPiRtdxwQXG9SLGPUJcy8kQAAAC1BnutFFSwj/wAAGSl8w2AgW1YZUd3NMS1nJmMFQoKjckkq/i6o6cLCi28AAwIAAAAnAZ8KdEf/AAAmqeru0S5KfD8gJRft0X259TShs1nzHRonVkF9NA7oAAAAMwGfDGpH/wAAJ9luOKwiEc4NKt1IQAlUoOWqJzJNjPwqE+jQqsLBDgdrn8WJrBgba4gg4QAAAD9BmxFJqEFsmUwIZ//+nhAAAE1DzXCxv2ADoG8Fkgo/6/g83BMIIUq1o9w8rC4PO/qayzAhvVmgHZ+l4+loeo0AAAA8QZ8vRRUsI/8AABkf6+i0PNPBi+X3B8HP/WyIrZGQA43Ukz+WLYa7q4qvu8Fht9ugjNc+ZPeZCclovd4vAAAAKgGfTnRH/wAAJ9tVbhIu993+adNcKFzCGggm1/h5wc4IYVS0gZaeLO4kGAAAACYBn1BqR/8AACfFU9M8eoZUhOfFhlJYfjLKYa8y9jagX4zqFa7UHAAAAEJBm1VJqEFsmUwIZ//+nhAAAE1DzYcunB/svmyVooKsLdK2Sv3+vI+Ak7AAHJZ8ApyKYH5zatQKcxrPG9UbHtc9SdsAAAAoQZ9zRRUsI/8AABkf6+i0PNPBi+bCp6/7TbalGGlzTTij4GWQgujpgAAAACYBn5J0R/8AACfbVyYoVsmrslBjSemBuQjUJpOJyAsmVu1Z/oE6SAAAACABn5RqR/8AACfFU9M8eoZUhOZAZScgzxcObawomBO7pwAAAGdBm5lJqEFsmUwIZ//+nhAAAE1D6NkWAHHtzSVWOjw+UDy25E6jib8AiceHIqf+aiPo1k2c0rVycS3BJPwEqdot3VgvDMv9AHi/25gLxKQ7kTKGBmo/BF4A+JfBsebg4XJ1yEydQdKAAAAALEGft0UVLCP/AAAZKJtmBl+hSC1Q5xS8iKLcL6f8zMII7dog0AhTmeqWN/spAAAAKQGf1nRH/wAAJ9A7uECHFYWHMzsqy6hzA5sPTAnadADf/AokzV+QWXFhAAAAJQGf2GpH/wAAJ8VV4fNkjxWk6/1Um+Qcvf5L/FYenhYMrnhErbcAAABxQZvdSahBbJlMCF///oywAABQKfEeZ5wHPgA6JNDutMXX0s7Jy9aykQjTUYowPHtJcMbtLL4NFrHWQg2BVEH+xnKfgn0rg0CGAYTnQRXjYnG6b9CO+tNl2ia5Rsw/ZONH+UyUvobyyFqxhf3qW4YQ7L8AAABNQZ/7RRUsI/8AABnJgu2wBEJT8VUcNGZeAASPyJhI4ed8UaX1IEr8pXhzPKPnVG0ovDrI7tKmCQi3f6hpOw+yCZBtNgQ3exiGpfhJP/AAAAAnAZ4adEf/AAAn21gKInwm0BQ/aEdgVu+Q6vbmoBrp003uBIlkQqMXAAAANQGeHGpH/wAAKOVUg1OQOwonEicwnodVSkqBACxZ87NXvFQ/u8k60IGSu1rmsKYFcQwQAMrxAAAAT0GaAUmoQWyZTAhf//6MsAAAUDzxGNy/+/qJSErPY2aqFZUZIwJreSjsx2MNlgxxMRWAEEP9OcDWXe38iQSmRY6bU6baBGcmjn6FuvyvxVcAAAArQZ4/RRUsI/8AABm/6+i0AuQFG9WtEAMg53dbO3l5k+fD8ybxi7suwMwJIAAAACoBnl50R/8AACjfnt3/NUI0ld7qEx/j7BySI0JJ/UglzeyxFbR8KVtv7LEAAAAqAZ5Aakf/AAAo5VPTOSlQ+50NsDV0OPozPfzjCBl/GROebW2m/5oP/2WAAAAAbUGaRUmoQWyZTAhf//6MsAAAUCnyuQAR2tRNErA+gYWv3QNBwkA70CPH6UZ3sI+tEdl1EBkqXLz/oM85oVCB8uT/ewnqVa0J0lpxdqqvQFkUZTF/+36xNMarfnvE6VJJwfZd44VNI6FnTp+/ZHkAAAAtQZ5jRRUsI/8AABm/6+i0PNPBi+bVxmuRt1QAC6npP0cDtd5UUv1NWdr0/DssAAAANgGegnRH/wAAKPtWONQPY6AEsI9sgtwCWKdk7Z1yhHt/2pxcGelru58lwVZ5GGVmynouKLdlgQAAADQBnoRqR/8AACjlVBAeJIQAszZE2n/DHcK1SMAhQ9bihrZ7XndKiLUZJ5GVQeAGNW6Fr2BBAAAAVUGahkmoQWyZTAhn//6eEAAAT2zf9QA3U827DmXq87IiIPjY1R9l8itlGVGYNboojnm+ysFs7oXqsw5MuY3uSH/1IOq4xeqc7ajGav+HlEfgUPg5ivEAAAA7QZqqSeEKUmUwIZ/+nhAAAFIOtrW6L9g+Eh7sIZjNK/t3YD3QeUQn8VLvw6gBbDFVbf6PeMEkdVG9dWUAAAAwQZ7IRTRMI/8AABppfMNgIFtWGXiAJD523cqiBGE0XWAm7DtbQnrWmHKqtWxRx/lgAAAAIQGe53RH/wAAKPqeBLYfPB96Z2yIc9FIpA5feluzczO+4AAAADIBnulqR/8AACoZbjisIim0AJRXu6fR00lKOfHoDiV1V6zD5nVVM+203CJWB72ZQPj+WQAAAFBBmu5JqEFomUwIZ//+nhAAAFIXsoATOAOYfcXYx169/UJZykdP25cznXJmJWWtSHhQ/rWIFEF6maJI90KVhXL8xWp2FkfBP3q27Xw+9LSlawAAAC5BnwxFESwj/wAAGl/r6K/2MCnhrUskdFxpy4BfkT0YtkTEALe+WbawOGGImwSYAAAAJQGfK3RH/wAAKhtXJf4407gAAuj+7TB8i+XJKp4YyIW6rR7QXN0AAAAzAZ8takf/AAAqBVPTPHqGWs4gAuogt/eRRD3KHR1e5YVjddutCKENGUPV9TUdC/H9K/OHAAAAdUGbMkmoQWyZTAhn//6eEAAAUat4rDIghvfh9Cy+vYpajf8AMNdiYCNK5EER5K9KgzXLJQW63PwMeGWeTM2EiRUFvWNjVkYmOaPyisgIVljay0kfIQBtUZ9tuOWGnCJZkVfCWwR5/Ne0i4ny4XdcsFOYvtLQPwAAADxBn1BFFSwj/wAAGlAhi2FhNtIrbz7XNIQmo2ursafCInbpjRkAJkeg4W4m8W7dAZJZiYhaJ8oIDrAqu4AAAAAzAZ9vdEf/AAAqAFjeU22SARyqFlFpNfRHwAWn9vfU0VtPZjuZ1vk9xZYaoSaI/lR6uMMuAAAANgGfcWpH/wAAKgVUEDPKmIAWa4UqdP8EqIE48RfmskWsM9YAJ1G3FsdD5zRa46B3bFTZWITV3QAAAHRBm3ZJqEFsmUwIZ//+nhAAAFGreKyoL+Bo76bRwALTo0Ov+1FD52Ij3ukwz+Fs6WdwZLNgLqJb1L0ZAXv4yienBlifmNi6HGKOp5t1IlKyk9SmcU6vbEQ1dxoZLG/uNl1qrQShH5ztTyJg+Yk35WpRW74irQAAAD5Bn5RFFSwj/wAAGm15hxncgATm0Gd/9sBYk2iUt8mOskgg/N3jusvVcOoJDGkeD22agqGN6XogAFMHO/Sa2AAAAC8Bn7N0R/8AACobWAoiVziCdIAFx5Ry1N4hJscUyVg8LlJNVGriR4JxPJcr67a1QQAAADYBn7VqR/8AACoFVch6QVwAXQAaI5/YhYNEj/9PQxJ3MnFLLeZHvL1LEVlFc5ZOaJC3W2mHfpAAAABeQZu6SahBbJlMCGf//p4QAABT63ia2CrwC1stxYcjLcYBgIxY8SkqOsU+E6MdniFWMseB17kqfAEZ+H+pMbnIO9zJdtSipsp6oTjgOjLyL1npE+/L9BJkI5p3HdC8QQAAAD9Bn9hFFSwj/wAAGwibUK7/bZoRYQBzcNjrUyyGAmIOC+LOXPAqUo6pFY4X4WxXf7lo2GGV3rm0hJZIdgUg9WEAAABEAZ/3dEf/AAArO1cmKFbKVEpcLvAYAVZ6qsDZ3k+Y7CFxqm07NjvVb+qBARqEAVrm/uCQzyQGOy7yhpyZd+isVW3RwssAAAA1AZ/5akf/AAArNzMdcZlDKXxAKWJBpqXoauQAtnLt6lWFh9/ExBMdaKePynrYwDNxRncMb+EAAABhQZv+SahBbJlMCGf//p4QAABULYko8fKf+nKufC+YvoMaLYbTyS0AHD/GiD8n1X3t2Uxuz/jFGIc0qLWy6az0r+oL/l3mz122bdJoteQ29PMRgw/Ty7AY/Ubvmb6ol/uAsAAAACxBnhxFFSwj/wAAGw15hhiPpb4wsACgPpuEUFxRX+pf+U0xoFOTxIK9Iac38QAAADMBnjt0R/8AACswO7HETogeCBcIqWTABY/29bO47hgHga/jIdf/ekrs9sbkut6kB7XwMeEAAAA2AZ49akf/AAArJVPTPHqFVnVySTGixirksDRn4AW3V23nYBT/m2WkYZgktKZXkzvcxK67Y/WoAAAAW0GaIkmoQWyZTAhn//6eEAAAU+t4rKI0rdADdTJ4WsMrz82I0+y3QNTE8dw1NlFJLlhZYNc3fyLDuSmZZZEFkkTHc3VSX/QEqo94zwfFu1nqz9ZgN+YtbAXSkdAAAABCQZ5ARRUsI/8AABr/7ImTi8byObULi+AWkCckNWAGCuh91PyLuieRcRrnqR2vG1xEVdxM0WWO4OsXzt0sREq92WUFAAAAJgGef3RH/wAAKztYCiJ8GdnzpMtjxpAWGxdybDv1yEcUNi/HAb+AAAAANwGeYWpH/wAAKyVV4fL/j8kaj/AASxc0Xnn0hmNndr2Tl2/CyqYXubwDx5IyunkMe+36kF3NFBEAAAB0QZpmSahBbJlMCF///oywAABW6jI4k3p6AHQNJ76gszmXz24/6ohF4MFtQzUCKrTbx8rJt5JiE3nf7eMVc0a1krg7n9PM26YmI49FD61M+LiQRLy1CIJbZ7cnK2CWf3bzv/ctj/xe/2z/F0WWbmQ0q4/Mx0AAAABwQZ6ERRUsI/8AABufQ+jTD6bz0A5dEkw503/TiuSDHINo0e4YlGm4Y/dtq584sxF/ZY+xuKcQ73WvSsKxR1Pul73qvkX0C7smNRrfYuBx6vyy7M5NYSlZ1uPEVkhH9Bn8lQ/Lt7p6RDTE7Jjb4W21MQAAAEABnqN0R/8AACxAWJmqDCxwAcenhiYRfNPIqDq2H+RqHPE4pw8yKyEtoKWM2UVRLfU9hOPClFIf7T1sJgxytfQ/AAAATQGepWpH/wAALEVSw9D6rQ5AAuolux9YMcu9A3gfOh0aFx1fTyR8QiiKi7Bxs+8pxS2I4KIFySS24VBl0aCwOwQEUg8ImjOEkxTgdtgxAAAAaUGaqkmoQWyZTAhf//6MsAAAVuoyVtBY42OjqYoALhpI6vju7WpzxD2BB1dk06GsW5cyn1mxuIPfst8aYKpUwD4G51vmpFvoqIxTLZghoaGliQypeA8tnH1m+3ejFsWalvtrzdqArA4YcQAAAE1BnshFFSwj/wAAG59D9T0JLtRwAmk/IwOpAtAIXRIs9s6vpCZycoMHsDXz15qJYttNwSvEFjDu9aB8WdqUZHna+M9F4LSNJ9EOt+q0GAAAAD4Bnud0R/8AACxawpY5dABFqme/Afmr83qHUoI0u0gHE1hvKanhZfU0XVH4EWcnW8dlHkkmTNrCcrn4uH0HoQAAAEQBnulqR/8AACxEGTPI1PokauUWnHiwAFxPpuUlMDzW5nIxJ8xphzOj+s0uc69ZjAJA8GMbtR36GgA183HV4EaxIym6rQAAAGZBmu5JqEFsmUwIX//+jLAAAFkqMkOrJiArzdgYaAkbYyH5dUEJo9tMHZ19WaTW7sXopwAYGPDvxgaOH0k1ZNQvtxwCjZVFWKNH4a5DSY3goXTQTdh0m9YgvfMLVG3rtOmg2MmTTtQAAABQQZ8MRRUsI/8AABxYersFOCGzeQtH+96nb7NtHG60WLNI94T9wp8YSsrlM5VS7R3QpUFV2oxMX9GjbhAm0AFtLqzZr1eB7b1CqvU5ryA+D2UAAAA4AZ8rdEf/AAAsWyAxABxrqUcEkCHlEJclS254PFFeKyzWoD8cltkPVVlIPHRBmX6pkppveBEnTqsAAAAwAZ8takf/AAAteW44rCOz9mvDlejkJu6MuCkUS7fyxmbD4t5UMhHZNRQhLIl/DF+BAAAAkEGbMkmoQWyZTAhX//44QAABWWLDgOAVp3S3XhNn7Pb2V6o8IDF1e2JM1vYV6xsGu/3lAhjUjJBJZcNQ7LqgGYiCCDkBns/D5GPrDV79pLQ7675p4OCPw6J7wt0ithyrL4a6o5on/ER/f9eGRl65Y2EN5C42wbvXD0A1r6/859tO8MtN/HrxgqzvMKO559/OlwAAAE9Bn1BFFSwj/wAAHEx9qeaBAAjBxmn57YSVVzahYdSRoWKEoegFkxklG39Ej5YEgXYQDLAnnh/XM14rZPkVy4HqqPjs08wCj+szCjCpKhZQAAAAOgGfb3RH/wAALXqdjWInzteBldbiMvIe4dW5nD87MtHe0TDnKWIbCdSQ67kh+jRipUUyZ4M7eAGuqggAAAA1AZ9xakf/AAAtZBoi5JcGXQBqAJyUdQJ6d0IfKajEEMIjaBHYBkJTBUbgJhU4tFjFy8cCqEEAAABUQZtzSahBbJlMCFf//jhAAAFZAJV2XIxwibC4Xbxg2Ekpak/JvRNBl+poFq1yq9TycQfkYFJRA4jaH1+/LF4yAIfAJWjuozen077sZ4GHNXiSX01YAAAAWUGblEnhClJlMCFf/jhAAAFYj6dLIKIFIVGMgXvk87t+aqXYJzGqYjoXUXSl9KA4p+e2SU66BF95pI5bNIJMUvRjoKAZcakGlS0iUw3bOXIcsUld342RFYuwAAAATEGbtUnhDomUwIX//oywAABZQrfo0ad0eEpXyL1Tb0hbyZhN+e2Da1yDxVXPcmAOESqqrr8CwArGkG2bP0/riXcWGacfbsBEfGsQo/0AAABhQZvWSeEPJlMCGf/+nhAAAFihSexrQAOGxqyGh4812K1TysfuJ0RVL3sd759JA3DxnBatp/SoBk254IAHdSC4OHGpMOvUYNirjK6PmtpTEDApShOZbe46lMngPBcP+LJ3sAAAAHZBm/pJ4Q8mUwIZ//6eEAAAWrvPI0gmgCsRygdVdU+tDKnqRfy1Hp8jDjs/yokjSTlZj3XXqso5viBKQEJeFyht4mBImtV/Kd0JXc2iSNczv2rAhlGcF0qi9oWksXqVxRhhjWHK/+4P/6IpeQpo9smHKa1Qqm6BAAAAV0GeGEURPCP/AAAc/H2Kx09DAOIJrmr3ZracAeTaoYFNkoxIv15X78AFm+AEvAS0c03a4FnI6AaZK838onpQATt1JkMxZSfXfZ7S0zXwj0nzsBT/RqkTWwAAAEEBnjd0R/8AAC6eKe81lghX/L10QxKTOEeCqpIjXorF3TX3EeDyDGLgavJAAfzF+a4MOPW8tgS0VaWK+vNnTCJfwAAAADwBnjlqR/8AAC6EGe4fOck25WJ4A+JWaGjrWU11lm8R3CEAMm5CdNDiP2Uw0oE5Pu+/qADzUTxFGKgIS8EAAAB9QZo+SahBaJlMCGf//p4QAABau9PpxABEhu9v27lJIU+xeQpQlUaUXWb9P9SslULqSofMHSYl7fDJ6Y2L9+d7/B/nAAg+R2nksWOo8WuUiphfBlSWK/BnjzS/YUxq34GKzedNYtRq9PLa8Rm+hCVuD7lUzUsuvcDxXogh1YAAAABBQZ5cRREsI/8AAB0NVhV8lZcuqvs81POUjqSqo5rL/3lVCs/Z8rDot/PqyhiaGvf+mAZlcFkfkovgzjK/UBlQOl0AAAA2AZ57dEf/AAAudMk95zB6vEBUAq3DlbTG2F3335Vr8AzK3KAZJpa/pHmTPIL66Rf5SpsEPV6XAAAAMQGefWpH/wAALoJITy+4ZHSDdneysIuc5dvCKJYzZg5a6xfixuUK5mtWO+wEgA2Y7VMAAABKQZpiSahBbJlMCGf//p4QAABdTsAz8ohOb9WjPA7Xuoe5+c1nwE6Hht7P/U3Em6CiGe0B8HJmXl8FIf8zLQW429SKTTE27p/73SAAAABAQZ6ARRUsI/8AAB2sfSHuUfciPpEkGhscurV6ZJJt4RQGSeem19Yebmzf1CTCNP/7a/eP/sfbIp83iHP9DZDamQAAADsBnr90R/8AAC/Xa1AyWZhqUhsecEaL1aC/PPHGLak94U0+VSHRMymzEuJ34AFuAKp24MO90ZKLl8vQ3AAAADkBnqFqR/8AAC+43MfpC40BxezalDX61pD+cSmcGub9EaUfknvjc9hgqHMQAJeqtgIz40qT5awr7qsAAACIQZqmSahBbJlMCGf//p4QAABdTsD/mgAjABj6ZWHXJYdngg1Kk8Tmqba6I0cX4Hol4ykAYuxjJyQnZ+4xBVLC1Fp1nBteF2bpWiyebiJ0lrJN31K1yhV2qpKG/tEiPktek0jPBmYTCPKTKxPGPjCjJHf4F+52nSVq3dOOUncpw+F7Xen3zps1wAAAAEZBnsRFFSwj/wAAHbb3h0ZgmfBNwylQc/CSM6pqN3qxjq7a51wlqm5ypk4MgAcDpr+5j5rjECs+6Ax9SkkFufRH0LuYPiFTAAAAPgGe43RH/wAAL9LF+wbxWNRrL+s7wOyxUT4Fix8H//3InngiRmg1A4AJzS68S+ooQg+fn88t0iWCEuFNsed/AAAAMQGe5WpH/wAAL8+owfXfSVddtxKPB53SSNPqQ1jgqG9XXGFlwEJTa5B80lEn8pk32cEAAABxQZrqSahBbJlMCGf//p4QAABfhGfduNjRwu5N2EAQsbW4+oBCVTZ+W0YHPpLGQ6Vwd9Eb3mwiPQ4/wzn3t+wsZlF7rdIO5By68oKOCORC/Q5v9ROOw2xE9+Cx+k3ztuwps76HKbeF7wfbbHVljcCQxNUAAABKQZ8IRRUsI/8AAB5ckGSC2HHtKCUBfAABGanoGogH0ULw2QVfU0VkPHD/9Qx5pywkJRxQVAJ4ZMXx2fIhmTjUtKFLqZ9Idw9LEfAAAAA7AZ8ndEf/AAAw9H+ioCO06iANuAMFxqymxgeJxUXbNO/8ALc2mwn43nF3+stp8GRn75cf5Kc2oQm5ujQAAAA6AZ8pakf/AAAw+NzH6RteMkvO6gXdI9uAX/olvteNJAliv3SOCijYfxtyvgAl6q2FV18AXnjNyQVa6QAAAIFBmy5JqEFsmUwIX//+jLAAAGKWcpPNdVnAQMfQIejO+hcIRoQfvBg8CFHW0NMLe4NS/7OxoSTbE6vYx8aq+96XlNtQxV9cB0H4Cce0CxDHrLKMXVFfNP+jC9DpbFs7VHfxKsMnRCEVJxMEueU8PMP51mj+5YHSjli24OitNMyGK+AAAABNQZ9MRRUsI/8AAB8YcgiyYLY0SZZa5vvXqqxksnPFAEU8pqjaypoIcjpCopQ0pb06EeiezBG0Xpj70euG2nx7Bfq/GK+zju+m/bLLoMAAAABCAZ9rdEf/AAAxETFaJWrIPQSAHiwT7B6Fo8F1AkweRHyqNbptiWdcADaKUU0gAAswXzvz2xAK9Z+v1z13sroTxvQZAAAAPAGfbWpH/wAAMlIsxQ8sYFE7oBzGudR00IQphoqSB3JI4pqbzH5+e7c0TSw1FE209zXEp+SJYQvVZfNrHQAAAHJBm3JJqEFsmUwIX//+jLAAAGKWeJW09aPpVeRzFC88Y8qgKtOqemIOEAAIym8bJIVGojrrKnQtMdb2A14iyTAzq7UfxnOb8Z/ZHSElGfE94usv8HKWSGlVFVTEFEljBHyDGf6fpHJ+LnlwLhnsFkoQdLEAAABSQZ+QRRUsI/8AAB8Mnm+It+eBMBgJH7Nsy/S5pqvMAxbuqnUxCsG5a2J/dICJg/nwSgAe69l0jW2NiY7HIRANHTcHlkzaBQG+J/lM7rae6NMTgAAAAEEBn690R/8AADJRMTidLw+pEBRwDPmqfXESG9dOH+NwaYwndkPphuPv2BOe8ONhCXo0sAHztat0bkFwP0/lUnUVOAAAACsBn7FqR/8AADI3GBpIl8VA1xq1yray6WZLvrYZDQHoStP2Djl3argNS8pvAAAAekGbtkmoQWyZTAhf//6MsAAAZS7F5lp3fnZjyB/Ti5obDrI0fzwA0QPUzdoo543bv9eN7ZMeuKUlbjn9JliIwee3j8n06RFWSi8iCV62NJ8DgZRoP/hD9ZFQ7XP7s/E4v8yXb8gBsLWPMyoqcw+Ncel1ocHzj5/iQNk4AAAAYUGf1EUVLCP/AAAfvJ5lTvlZu3l6PQ+nVnUYkDeVvQm6hKGGMcVipstxkyWO7zQOgaHM4K8eOQZI46iq6HNuT6j5x7vgIuKjhCGt2A3W2oZHhnY+f5MpHJVG7pBa0pg9aYAAAABGAZ/zdEf/AAAzl2tQMrcQ1Qpd8VRuem/UU3zKBcLDIvsVaZEqXiOD8N208q7hAATmm1ho2vn/7gK+hf/yhZFtBvMzG9dXoQAAAD4Bn/VqR/8AADN3F7b2YzMIGG6TqCn+8oCHdXJ/ytzEr5upeI0yFXTgE9SYZp8WrrVAAnGhDYqpHnyQZgX9GgAAAGlBm/pJqEFsmUwIV//+OEAAAZE+C7b5PoCIhKhDxnbzE9yBU3QCE4knwtMGwo/LKUeZLsAP2HvaOsmGhrge1dX9bh6Skr42502M+qhOEKFYDA/cS74DtO9cqPCLpDJ/4jbn+SholBSCOlkAAAA8QZ4YRRUsI/8AACC8eQqkwV56aJ2a2xz3LMyAPkAjmwfxK46W1I7WtXAAJLdVnS1Wqq9Gd3KrfHvRIMpfAAAARAGeN3RH/wAAM5F9GiUhyEdCj8WmuswrZDr6yNX3zMj+kualZh4B3sABCnQqAdsqzU8EKiGJeQGSvTVSl4QMIs8HI1mAAAAAPwGeOWpH/wAANLcX+DoW9CgAZ7aaxpwpsUsZOPp8Y9tQgyLd7P4G6NtydAL/Bb3LsyHqIVz27o5f/GJmWbKswQAAAIJBmj5JqEFsmUwIR//94QAABhxU6Dod38+2UbhGxpgKhU22Kck9zFk5wrbAI3XmxnIsd8kCIKonSGffS2r/9H1PUzOLjlwtJvcGM5xmWTTCP4hpXT6zmpu3iQdZc21VSl6Mjymz9W4sGEI+OJMncMK7pCGVlr6OppjBIwtIsfIb7AdAAAAAOkGeXEUVLCP/AAAgq/KQ3o6ody1kGVKLLGu23NGPg9zKBLtzQ66u8gxbjREJutTcex+xuYoalMCgQYEAAABKAZ57dEf/AAA00Xz4nOy9JTd//3spOy7y5g1cs5Czhe24OGkvNGlRKC/YfaCDqao3gLjBEFdPYAPa0P20L+woASbhaHx6E2SX26EAAABEAZ59akf/AAA0txij53XrSzJxbr1NxebEdFNdqAIY8kcA5Ze/9EuiSJcO9DVS4SbiZ00GKfGArgtWb/urX1amauP5qkAAAABsQZpiSahBbJlMCEf//eEAAAZEVdzIABXmcb8qeiBiCoPQmxeK7gdSLkyGH2xzIKHwbjBYYJnBEOoB7JodHU8jMzqrk6IXefE8yuv1qAog8SMyhA87raEuvNLaXhMDjyvHyRZlSKT53MPmrZ/LAAAAUEGegEUVLCP/AAAhuojWHuz+u++Wo+wdm7t1tS6kyOkkAPcI/XRmUALF3O7wYFKjaGXd5Lx7CJUb2Oln3Z26+nqv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type=\"video/mp4\" />\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos = np.array(env.videos)\n",
    "n_videos = 3\n",
    "\n",
    "idxs = np.linspace(0, len(videos) - 1, n_videos).astype(int)\n",
    "videos = videos[idxs,:]\n",
    "\n",
    "strm = ''\n",
    "for video_path, meta_path in videos:\n",
    "    video = io.open(video_path, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    \n",
    "    with open(meta_path) as data_file:    \n",
    "        meta = json.load(data_file)\n",
    "\n",
    "    html_tag = \"\"\"\n",
    "    <h2>{0}<h2/>\n",
    "    <video width=\"960\" height=\"540\" controls>\n",
    "        <source src=\"data:video/mp4;base64,{1}\" type=\"video/mp4\" />\n",
    "    </video>\"\"\"\n",
    "    strm += html_tag.format('Episode ' + str(meta['episode_id']), encoded.decode('ascii'))\n",
    "HTML(data=strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 20:09:16,888] Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/tmprkc4htq8')\n"
     ]
    }
   ],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-04-26 20:09:17,431] [CartPole-v0] Uploading 100 episodes of training data\n",
      "[2017-04-26 20:09:18,139] [CartPole-v0] Uploading videos of 5 training episodes (74783 bytes)\n",
      "[2017-04-26 20:09:18,611] [CartPole-v0] Creating evaluation object from /tmp/tmprkc4htq8 with learning curve and training video\n",
      "[2017-04-26 20:09:18,933] \n",
      "****************************************************\n",
      "You successfully uploaded your evaluation on CartPole-v0 to\n",
      "OpenAI Gym! You can find it at:\n",
      "\n",
      "    https://gym.openai.com/evaluations/eval_VxzNknfHSOO1ukCE89lcNw\n",
      "\n",
      "****************************************************\n"
     ]
    }
   ],
   "source": [
    "gym.upload(mdir, api_key='<YOUR API KEY>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Good??\n",
    "\n",
    "Good enough????\n",
    "\n",
    "Perfect??????\n",
    "\n",
    "Just so you know, this guy can average 200 points, which is the max. If that is not the case, keep trying!\n",
    "\n",
    "When you are done comeback to the next notebooks. The topics coming later are a bit more advanced, but I'd like to at least give some intuition. If you are onboard, great. Let's move on."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
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